diff --git a/README.md b/README.md
index f33894fe610a3dc4f06e59d0cdc59b1582d97ab4..1f81b85c8339f6f22a1fe8870d2c84d300319edd 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,2 @@
-BCQ - commit - [4876f7e]5afa9eb2981feec5daf67202514477518
\ No newline at end of file
+BCQ - commit - [4876f7e]5afa9eb2981feec5daf67202514477518 - https://github.com/sfujim/BCQ
+baselines - commit - [b99a73a]fe37206775ac8b884d32a36e213a3fac2 - https://github.com/openai/baselines/tree/tf2
\ No newline at end of file
diff --git a/baselines/.benchmark_pattern b/baselines/.benchmark_pattern
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/baselines/.benchmark_pattern
@@ -0,0 +1 @@
+
diff --git a/baselines/.gitignore b/baselines/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a41103d0b7c62c8942ff9320c210e3d13ba93712
--- /dev/null
+++ b/baselines/.gitignore
@@ -0,0 +1,36 @@
+*.swp
+*.pyc
+*.pkl
+*.py~
+.pytest_cache
+.DS_Store
+.idea
+
+# Setuptools distribution and build folders.
+/dist/
+/build
+keys/
+
+# Virtualenv
+/env
+
+
+*.sublime-project
+*.sublime-workspace
+
+.idea
+
+logs/
+
+.ipynb_checkpoints
+ghostdriver.log
+
+htmlcov
+
+junk
+src
+
+*.egg-info
+.cache
+
+MUJOCO_LOG.TXT
diff --git a/baselines/.travis.yml b/baselines/.travis.yml
new file mode 100644
index 0000000000000000000000000000000000000000..b8bccbb0a3ea18d0918362e9660aa3066548792f
--- /dev/null
+++ b/baselines/.travis.yml
@@ -0,0 +1,14 @@
+language: python
+python:
+ - "3.6"
+
+services:
+ - docker
+
+install:
+ - pip install flake8
+ - docker build . -t baselines-test
+
+script:
+ - flake8 . --show-source --statistics --exclude=baselines/her
+ - docker run -e RUNSLOW=1 baselines-test pytest -v .
diff --git a/baselines/Dockerfile b/baselines/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..12e67be483604760eb2f04860dbb9920b2acdfbf
--- /dev/null
+++ b/baselines/Dockerfile
@@ -0,0 +1,18 @@
+FROM python:3.6
+
+RUN apt-get -y update && apt-get -y install ffmpeg
+# RUN apt-get -y update && apt-get -y install git wget python-dev python3-dev libopenmpi-dev python-pip zlib1g-dev cmake python-opencv
+
+ENV CODE_DIR /root/code
+
+COPY . $CODE_DIR/baselines
+WORKDIR $CODE_DIR/baselines
+
+# Clean up pycache and pyc files
+RUN rm -rf __pycache__ && \
+ find . -name "*.pyc" -delete && \
+ pip install tensorflow && \
+ pip install -e .[test]
+
+
+CMD /bin/bash
diff --git a/baselines/LICENSE b/baselines/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..57f53c30e81d5e33c6e423cbde23718f0f2be143
--- /dev/null
+++ b/baselines/LICENSE
@@ -0,0 +1,21 @@
+The MIT License
+
+Copyright (c) 2017 OpenAI (http://openai.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/baselines/README.md b/baselines/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..932e822b360f1fe49275381e5d6adc926bc44c0e
--- /dev/null
+++ b/baselines/README.md
@@ -0,0 +1,160 @@
+**Status:** Active (under active development, breaking changes may occur)
+
+<img src="data/logo.jpg" width=25% align="right" /> [](https://travis-ci.org/openai/baselines)
+
+# Baselines
+
+OpenAI Baselines is a set of high-quality implementations of reinforcement learning algorithms.
+
+These algorithms will make it easier for the research community to replicate, refine, and identify new ideas, and will create good baselines to build research on top of. Our DQN implementation and its variants are roughly on par with the scores in published papers. We expect they will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones.
+
+## Prerequisites
+Baselines requires python3 (>=3.5) with the development headers. You'll also need system packages CMake, OpenMPI and zlib. Those can be installed as follows
+### Ubuntu
+
+```bash
+sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev
+```
+
+### Mac OS X
+Installation of system packages on Mac requires [Homebrew](https://brew.sh). With Homebrew installed, run the following:
+```bash
+brew install cmake openmpi
+```
+
+## Virtual environment
+From the general python package sanity perspective, it is a good idea to use virtual environments (virtualenvs) to make sure packages from different projects do not interfere with each other. You can install virtualenv (which is itself a pip package) via
+```bash
+pip install virtualenv
+```
+Virtualenvs are essentially folders that have copies of python executable and all python packages.
+To create a virtualenv called venv with python3, one runs
+```bash
+virtualenv /path/to/venv --python=python3
+```
+To activate a virtualenv:
+```
+. /path/to/venv/bin/activate
+```
+More thorough tutorial on virtualenvs and options can be found [here](https://virtualenv.pypa.io/en/stable/)
+
+
+## Installation
+- Clone the repo and cd into it:
+ ```bash
+ git clone https://github.com/openai/baselines.git
+ cd baselines
+ ```
+- If you don't have TensorFlow installed already, install your favourite flavor of TensorFlow. In most cases,
+ ```bash
+ pip install tensorflow-gpu # if you have a CUDA-compatible gpu and proper drivers
+ ```
+ or
+ ```bash
+ pip install tensorflow
+ ```
+ should be sufficient. Refer to [TensorFlow installation guide](https://www.tensorflow.org/install/)
+ for more details.
+
+- Install baselines package
+ ```bash
+ pip install -e .
+ ```
+
+### MuJoCo
+Some of the baselines examples use [MuJoCo](http://www.mujoco.org) (multi-joint dynamics in contact) physics simulator, which is proprietary and requires binaries and a license (temporary 30-day license can be obtained from [www.mujoco.org](http://www.mujoco.org)). Instructions on setting up MuJoCo can be found [here](https://github.com/openai/mujoco-py)
+
+## Testing the installation
+All unit tests in baselines can be run using pytest runner:
+```
+pip install pytest
+pytest
+```
+
+## Training models
+Most of the algorithms in baselines repo are used as follows:
+```bash
+python -m baselines.run --alg=<name of the algorithm> --env=<environment_id> [additional arguments]
+```
+### Example 1. PPO with MuJoCo Humanoid
+For instance, to train a fully-connected network controlling MuJoCo humanoid using PPO2 for 20M timesteps
+```bash
+python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timesteps=2e7
+```
+Note that for mujoco environments fully-connected network is default, so we can omit `--network=mlp`
+The hyperparameters for both network and the learning algorithm can be controlled via the command line, for instance:
+```bash
+python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timesteps=2e7 --ent_coef=0.1 --num_hidden=32 --num_layers=3 --value_network=copy
+```
+will set entropy coefficient to 0.1, and construct fully connected network with 3 layers with 32 hidden units in each, and create a separate network for value function estimation (so that its parameters are not shared with the policy network, but the structure is the same)
+
+See docstrings in [common/models.py](baselines/common/models.py) for description of network parameters for each type of model, and
+docstring for [baselines/ppo2/ppo2.py/learn()](baselines/ppo2/ppo2.py#L152) for the description of the ppo2 hyperparameters.
+
+### Example 2. DQN on Atari
+DQN with Atari is at this point a classics of benchmarks. To run the baselines implementation of DQN on Atari Pong:
+```
+python -m baselines.run --alg=deepq --env=PongNoFrameskip-v4 --num_timesteps=1e6
+```
+
+## Saving, loading and visualizing models
+
+### Saving and loading the model
+The algorithms serialization API is not properly unified yet; however, there is a simple method to save / restore trained models.
+`--save_path` and `--load_path` command-line option loads the tensorflow state from a given path before training, and saves it after the training, respectively.
+Let's imagine you'd like to train ppo2 on Atari Pong, save the model and then later visualize what has it learnt.
+```bash
+python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=2e7 --save_path=~/models/pong_20M_ppo2
+```
+This should get to the mean reward per episode about 20. To load and visualize the model, we'll do the following - load the model, train it for 0 steps, and then visualize:
+```bash
+python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=0 --load_path=~/models/pong_20M_ppo2 --play
+```
+
+*NOTE:* Mujoco environments require normalization to work properly, so we wrap them with VecNormalize wrapper. Currently, to ensure the models are saved with normalization (so that trained models can be restored and run without further training) the normalization coefficients are saved as tensorflow variables. This can decrease the performance somewhat, so if you require high-throughput steps with Mujoco and do not need saving/restoring the models, it may make sense to use numpy normalization instead. To do that, set 'use_tf=False` in [baselines/run.py](baselines/run.py#L116).
+
+### Logging and vizualizing learning curves and other training metrics
+By default, all summary data, including progress, standard output, is saved to a unique directory in a temp folder, specified by a call to Python's [tempfile.gettempdir()](https://docs.python.org/3/library/tempfile.html#tempfile.gettempdir).
+The directory can be changed with the `--log_path` command-line option.
+```bash
+python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=2e7 --save_path=~/models/pong_20M_ppo2 --log_path=~/logs/Pong/
+```
+*NOTE:* Please be aware that the logger will overwrite files of the same name in an existing directory, thus it's recommended that folder names be given a unique timestamp to prevent overwritten logs.
+
+Another way the temp directory can be changed is through the use of the `$OPENAI_LOGDIR` environment variable.
+
+For examples on how to load and display the training data, see [here](docs/viz/viz.ipynb).
+
+## Subpackages
+
+- [A2C](baselines/a2c)
+- [ACER](baselines/acer)
+- [ACKTR](baselines/acktr)
+- [DDPG](baselines/ddpg)
+- [DQN](baselines/deepq)
+- [GAIL](baselines/gail)
+- [HER](baselines/her)
+- [PPO1](baselines/ppo1) (obsolete version, left here temporarily)
+- [PPO2](baselines/ppo2)
+- [TRPO](baselines/trpo_mpi)
+
+
+
+## Benchmarks
+Results of benchmarks on Mujoco (1M timesteps) and Atari (10M timesteps) are available
+[here for Mujoco](https://htmlpreview.github.com/?https://github.com/openai/baselines/blob/master/benchmarks_mujoco1M.htm)
+and
+[here for Atari](https://htmlpreview.github.com/?https://github.com/openai/baselines/blob/master/benchmarks_atari10M.htm)
+respectively. Note that these results may be not on the latest version of the code, particular commit hash with which results were obtained is specified on the benchmarks page.
+
+To cite this repository in publications:
+
+ @misc{baselines,
+ author = {Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Tan, Zhenyu and Wu, Yuhuai and Zhokhov, Peter},
+ title = {OpenAI Baselines},
+ year = {2017},
+ publisher = {GitHub},
+ journal = {GitHub repository},
+ howpublished = {\url{https://github.com/openai/baselines}},
+ }
+
diff --git a/baselines/baselines/__init__.py b/baselines/baselines/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/baselines/baselines/a2c/README.md b/baselines/baselines/a2c/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9450054021acf3af1abc8927d5c74ff162813b02
--- /dev/null
+++ b/baselines/baselines/a2c/README.md
@@ -0,0 +1,13 @@
+# A2C
+
+- Original paper: https://arxiv.org/abs/1602.01783
+- Baselines blog post: https://blog.openai.com/baselines-acktr-a2c/
+- `python -m baselines.run --alg=a2c --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options
+- also refer to the repo-wide [README.md](../../README.md#training-models)
+
+## Files
+- `run_atari`: file used to run the algorithm.
+- `policies.py`: contains the different versions of the A2C architecture (MlpPolicy, CNNPolicy, LstmPolicy...).
+- `a2c.py`: - Model : class used to initialize the step_model (sampling) and train_model (training)
+ - learn : Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.
+- `runner.py`: class used to generates a batch of experiences
diff --git a/baselines/baselines/a2c/__init__.py b/baselines/baselines/a2c/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/baselines/baselines/a2c/a2c.py b/baselines/baselines/a2c/a2c.py
new file mode 100644
index 0000000000000000000000000000000000000000..dacadaf16dee83325e8bccc9c9b77b97c96d5a6e
--- /dev/null
+++ b/baselines/baselines/a2c/a2c.py
@@ -0,0 +1,203 @@
+import time
+import tensorflow as tf
+
+from baselines import logger
+
+from baselines.common import set_global_seeds, explained_variance
+from baselines.common.models import get_network_builder
+from baselines.common.policies import PolicyWithValue
+
+from baselines.a2c.utils import InverseLinearTimeDecay
+from baselines.a2c.runner import Runner
+from baselines.ppo2.ppo2 import safemean
+import os.path as osp
+from collections import deque
+
+class Model(tf.keras.Model):
+
+ """
+ We use this class to :
+ __init__:
+ - Creates the step_model
+ - Creates the train_model
+
+ train():
+ - Make the training part (feedforward and retropropagation of gradients)
+
+ save/load():
+ - Save load the model
+ """
+ def __init__(self, *, ac_space, policy_network, nupdates,
+ ent_coef=0.01, vf_coef=0.5, max_grad_norm=0.5, lr=7e-4,
+ alpha=0.99, epsilon=1e-5, total_timesteps=int(80e6)):
+
+ super(Model, self).__init__(name='A2CModel')
+ self.train_model = PolicyWithValue(ac_space, policy_network, value_network=None, estimate_q=False)
+ lr_schedule = InverseLinearTimeDecay(initial_learning_rate=lr, nupdates=nupdates)
+ self.optimizer = tf.keras.optimizers.RMSprop(learning_rate=lr_schedule, rho=alpha, epsilon=epsilon)
+
+ self.ent_coef = ent_coef
+ self.vf_coef = vf_coef
+ self.max_grad_norm = max_grad_norm
+ self.step = self.train_model.step
+ self.value = self.train_model.value
+ self.initial_state = self.train_model.initial_state
+
+ @tf.function
+ def train(self, obs, states, rewards, masks, actions, values):
+ advs = rewards - values
+ with tf.GradientTape() as tape:
+ policy_latent = self.train_model.policy_network(obs)
+ pd, _ = self.train_model.pdtype.pdfromlatent(policy_latent)
+ neglogpac = pd.neglogp(actions)
+ entropy = tf.reduce_mean(pd.entropy())
+ vpred = self.train_model.value(obs)
+ vf_loss = tf.reduce_mean(tf.square(vpred - rewards))
+ pg_loss = tf.reduce_mean(advs * neglogpac)
+ loss = pg_loss - entropy * self.ent_coef + vf_loss * self.vf_coef
+
+ var_list = tape.watched_variables()
+ grads = tape.gradient(loss, var_list)
+ grads, _ = tf.clip_by_global_norm(grads, self.max_grad_norm)
+ grads_and_vars = list(zip(grads, var_list))
+ self.optimizer.apply_gradients(grads_and_vars)
+
+ return pg_loss, vf_loss, entropy
+
+
+def learn(
+ network,
+ env,
+ seed=None,
+ nsteps=5,
+ total_timesteps=int(80e6),
+ vf_coef=0.5,
+ ent_coef=0.01,
+ max_grad_norm=0.5,
+ lr=7e-4,
+ lrschedule='linear',
+ epsilon=1e-5,
+ alpha=0.99,
+ gamma=0.99,
+ log_interval=100,
+ load_path=None,
+ **network_kwargs):
+
+ '''
+ Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.
+
+ Parameters:
+ -----------
+
+ network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
+ specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
+ tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
+ neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
+ See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
+
+
+ env: RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)
+
+
+ seed: seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)
+
+ nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
+ nenv is number of environment copies simulated in parallel)
+
+ total_timesteps: int, total number of timesteps to train on (default: 80M)
+
+ vf_coef: float, coefficient in front of value function loss in the total loss function (default: 0.5)
+
+ ent_coef: float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01)
+
+ max_gradient_norm: float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)
+
+ lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
+
+ lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
+ returns fraction of the learning rate (specified as lr) as output
+
+ epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
+
+ alpha: float, RMSProp decay parameter (default: 0.99)
+
+ gamma: float, reward discounting parameter (default: 0.99)
+
+ log_interval: int, specifies how frequently the logs are printed out (default: 100)
+
+ **network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
+ For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
+
+ '''
+
+
+
+ set_global_seeds(seed)
+
+ total_timesteps = int(total_timesteps)
+
+ # Get the nb of env
+ nenvs = env.num_envs
+
+ # Get state_space and action_space
+ ob_space = env.observation_space
+ ac_space = env.action_space
+
+ if isinstance(network, str):
+ network_type = network
+ policy_network_fn = get_network_builder(network_type)(**network_kwargs)
+ policy_network = policy_network_fn(ob_space.shape)
+
+ # Calculate the batch_size
+ nbatch = nenvs * nsteps
+ nupdates = total_timesteps // nbatch
+
+ # Instantiate the model object (that creates step_model and train_model)
+ model = Model(ac_space=ac_space, policy_network=policy_network, nupdates=nupdates, ent_coef=ent_coef, vf_coef=vf_coef,
+ max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps)
+
+ if load_path is not None:
+ load_path = osp.expanduser(load_path)
+ ckpt = tf.train.Checkpoint(model=model)
+ manager = tf.train.CheckpointManager(ckpt, load_path, max_to_keep=None)
+ ckpt.restore(manager.latest_checkpoint)
+
+ # Instantiate the runner object
+ runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
+ epinfobuf = deque(maxlen=100)
+
+ # Start total timer
+ tstart = time.time()
+
+ for update in range(1, nupdates+1):
+ # Get mini batch of experiences
+ obs, states, rewards, masks, actions, values, epinfos = runner.run()
+ epinfobuf.extend(epinfos)
+
+ obs = tf.constant(obs)
+ if states is not None:
+ states = tf.constant(states)
+ rewards = tf.constant(rewards)
+ masks = tf.constant(masks)
+ actions = tf.constant(actions)
+ values = tf.constant(values)
+ policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
+ nseconds = time.time()-tstart
+
+ # Calculate the fps (frame per second)
+ fps = int((update*nbatch)/nseconds)
+ if update % log_interval == 0 or update == 1:
+ # Calculates if value function is a good predicator of the returns (ev > 1)
+ # or if it's just worse than predicting nothing (ev =< 0)
+ ev = explained_variance(values, rewards)
+ logger.record_tabular("nupdates", update)
+ logger.record_tabular("total_timesteps", update*nbatch)
+ logger.record_tabular("fps", fps)
+ logger.record_tabular("policy_entropy", float(policy_entropy))
+ logger.record_tabular("value_loss", float(value_loss))
+ logger.record_tabular("explained_variance", float(ev))
+ logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf]))
+ logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf]))
+ logger.dump_tabular()
+ return model
+
diff --git a/baselines/baselines/a2c/runner.py b/baselines/baselines/a2c/runner.py
new file mode 100644
index 0000000000000000000000000000000000000000..68d1c78e37cc4a422a1392f686f16dab22b3a523
--- /dev/null
+++ b/baselines/baselines/a2c/runner.py
@@ -0,0 +1,80 @@
+import tensorflow as tf
+import numpy as np
+from baselines.a2c.utils import discount_with_dones
+from baselines.common.runners import AbstractEnvRunner
+
+class Runner(AbstractEnvRunner):
+ """
+ We use this class to generate batches of experiences
+
+ __init__:
+ - Initialize the runner
+
+ run():
+ - Make a mini batch of experiences
+ """
+ def __init__(self, env, model, nsteps=5, gamma=0.99):
+ super().__init__(env=env, model=model, nsteps=nsteps)
+ self.gamma = gamma
+
+ def run(self):
+ # We initialize the lists that will contain the mb of experiences
+ mb_obs, mb_rewards, mb_actions, mb_values, mb_dones = [],[],[],[],[]
+ mb_states = self.states
+ epinfos = []
+ for _ in range(self.nsteps):
+ # Given observations, take action and value (V(s))
+ # We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init
+ obs = tf.constant(self.obs)
+ actions, values, self.states, _ = self.model.step(obs)
+ actions = actions._numpy()
+ # Append the experiences
+ mb_obs.append(self.obs.copy())
+ mb_actions.append(actions)
+ mb_values.append(values._numpy())
+ mb_dones.append(self.dones)
+
+ # Take actions in env and look the results
+ self.obs[:], rewards, self.dones, infos = self.env.step(actions)
+ for info in infos:
+ maybeepinfo = info.get('episode')
+ if maybeepinfo: epinfos.append(maybeepinfo)
+ mb_rewards.append(rewards)
+
+ mb_dones.append(self.dones)
+
+ # Batch of steps to batch of rollouts
+ mb_obs = sf01(np.asarray(mb_obs, dtype=self.obs.dtype))
+ mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)
+ mb_actions = sf01(np.asarray(mb_actions, dtype=actions.dtype))
+ mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0)
+ mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)
+ mb_masks = mb_dones[:, :-1]
+ mb_dones = mb_dones[:, 1:]
+
+
+ if self.gamma > 0.0:
+ # Discount/bootstrap off value fn
+ last_values = self.model.value(tf.constant(self.obs))._numpy().tolist()
+ for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)):
+ rewards = rewards.tolist()
+ dones = dones.tolist()
+ if dones[-1] == 0:
+ rewards = discount_with_dones(rewards+[value], dones+[0], self.gamma)[:-1]
+ else:
+ rewards = discount_with_dones(rewards, dones, self.gamma)
+
+ mb_rewards[n] = rewards
+
+
+ mb_rewards = mb_rewards.flatten()
+ mb_values = mb_values.flatten()
+ mb_masks = mb_masks.flatten()
+ return mb_obs, mb_states, mb_rewards, mb_masks, mb_actions, mb_values, epinfos
+
+def sf01(arr):
+ """
+ swap and then flatten axes 0 and 1
+ """
+ s = arr.shape
+ return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])
diff --git a/baselines/baselines/a2c/utils.py b/baselines/baselines/a2c/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c68de112e7d46a17398c5e562504445ac8368d13
--- /dev/null
+++ b/baselines/baselines/a2c/utils.py
@@ -0,0 +1,63 @@
+import numpy as np
+import tensorflow as tf
+
+def ortho_init(scale=1.0):
+ def _ortho_init(shape, dtype, partition_info=None):
+ #lasagne ortho init for tf
+ shape = tuple(shape)
+ if len(shape) == 2:
+ flat_shape = shape
+ elif len(shape) == 4: # assumes NHWC
+ flat_shape = (np.prod(shape[:-1]), shape[-1])
+ else:
+ raise NotImplementedError
+ a = np.random.normal(0.0, 1.0, flat_shape)
+ u, _, v = np.linalg.svd(a, full_matrices=False)
+ q = u if u.shape == flat_shape else v # pick the one with the correct shape
+ q = q.reshape(shape)
+ return (scale * q[:shape[0], :shape[1]]).astype(np.float32)
+ return _ortho_init
+
+def conv(scope, *, nf, rf, stride, activation, pad='valid', init_scale=1.0, data_format='channels_last'):
+ with tf.name_scope(scope):
+ layer = tf.keras.layers.Conv2D(filters=nf, kernel_size=rf, strides=stride, padding=pad,
+ data_format=data_format, kernel_initializer=ortho_init(init_scale))
+ return layer
+
+def fc(input_shape, scope, nh, *, init_scale=1.0, init_bias=0.0):
+ with tf.name_scope(scope):
+ layer = tf.keras.layers.Dense(units=nh, kernel_initializer=ortho_init(init_scale),
+ bias_initializer=tf.keras.initializers.Constant(init_bias))
+ layer.build(input_shape)
+ return layer
+
+def discount_with_dones(rewards, dones, gamma):
+ discounted = []
+ r = 0
+ for reward, done in zip(rewards[::-1], dones[::-1]):
+ r = reward + gamma*r*(1.-done) # fixed off by one bug
+ discounted.append(r)
+ return discounted[::-1]
+
+class InverseLinearTimeDecay(tf.keras.optimizers.schedules.LearningRateSchedule):
+ def __init__(self, initial_learning_rate, nupdates, name="InverseLinearTimeDecay"):
+ super(InverseLinearTimeDecay, self).__init__()
+ self.initial_learning_rate = initial_learning_rate
+ self.nupdates = nupdates
+ self.name = name
+
+ def __call__(self, step):
+ with tf.name_scope(self.name):
+ initial_learning_rate = tf.convert_to_tensor(self.initial_learning_rate, name="initial_learning_rate")
+ dtype = initial_learning_rate.dtype
+ step_t = tf.cast(step, dtype)
+ nupdates_t = tf.convert_to_tensor(self.nupdates, dtype=dtype)
+ tf.assert_less(step_t, nupdates_t)
+ return initial_learning_rate * (1. - step_t / nupdates_t)
+
+ def get_config(self):
+ return {
+ "initial_learning_rate": self.initial_learning_rate,
+ "nupdates": self.nupdates,
+ "name": self.name
+ }
diff --git a/baselines/baselines/bench/__init__.py b/baselines/baselines/bench/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecfa5372c2be36f4abe37cffe2928ca99a869e75
--- /dev/null
+++ b/baselines/baselines/bench/__init__.py
@@ -0,0 +1,2 @@
+from baselines.bench.benchmarks import * # noqa: F403 F401
+from baselines.bench.monitor import * # noqa: F403 F401
diff --git a/baselines/baselines/bench/benchmarks.py b/baselines/baselines/bench/benchmarks.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d626ded231572abc257a39db32f55e67aa9b6c9
--- /dev/null
+++ b/baselines/baselines/bench/benchmarks.py
@@ -0,0 +1,164 @@
+import re
+import os
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+
+_atari7 = ['BeamRider', 'Breakout', 'Enduro', 'Pong', 'Qbert', 'Seaquest', 'SpaceInvaders']
+_atariexpl7 = ['Freeway', 'Gravitar', 'MontezumaRevenge', 'Pitfall', 'PrivateEye', 'Solaris', 'Venture']
+
+_BENCHMARKS = []
+
+remove_version_re = re.compile(r'-v\d+$')
+
+
+def register_benchmark(benchmark):
+ for b in _BENCHMARKS:
+ if b['name'] == benchmark['name']:
+ raise ValueError('Benchmark with name %s already registered!' % b['name'])
+
+ # automatically add a description if it is not present
+ if 'tasks' in benchmark:
+ for t in benchmark['tasks']:
+ if 'desc' not in t:
+ t['desc'] = remove_version_re.sub('', t.get('env_id', t.get('id')))
+ _BENCHMARKS.append(benchmark)
+
+
+def list_benchmarks():
+ return [b['name'] for b in _BENCHMARKS]
+
+
+def get_benchmark(benchmark_name):
+ for b in _BENCHMARKS:
+ if b['name'] == benchmark_name:
+ return b
+ raise ValueError('%s not found! Known benchmarks: %s' % (benchmark_name, list_benchmarks()))
+
+
+def get_task(benchmark, env_id):
+ """Get a task by env_id. Return None if the benchmark doesn't have the env"""
+ return next(filter(lambda task: task['env_id'] == env_id, benchmark['tasks']), None)
+
+
+def find_task_for_env_id_in_any_benchmark(env_id):
+ for bm in _BENCHMARKS:
+ for task in bm["tasks"]:
+ if task["env_id"] == env_id:
+ return bm, task
+ return None, None
+
+
+_ATARI_SUFFIX = 'NoFrameskip-v4'
+
+register_benchmark({
+ 'name': 'Atari50M',
+ 'description': '7 Atari games from Mnih et al. (2013), with pixel observations, 50M timesteps',
+ 'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 2, 'num_timesteps': int(50e6)} for _game in _atari7]
+})
+
+register_benchmark({
+ 'name': 'Atari10M',
+ 'description': '7 Atari games from Mnih et al. (2013), with pixel observations, 10M timesteps',
+ 'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 6, 'num_timesteps': int(10e6)} for _game in _atari7]
+})
+
+register_benchmark({
+ 'name': 'Atari1Hr',
+ 'description': '7 Atari games from Mnih et al. (2013), with pixel observations, 1 hour of walltime',
+ 'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 2, 'num_seconds': 60 * 60} for _game in _atari7]
+})
+
+register_benchmark({
+ 'name': 'AtariExploration10M',
+ 'description': '7 Atari games emphasizing exploration, with pixel observations, 10M timesteps',
+ 'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 2, 'num_timesteps': int(10e6)} for _game in _atariexpl7]
+})
+
+
+# MuJoCo
+
+_mujocosmall = [
+ 'InvertedDoublePendulum-v2', 'InvertedPendulum-v2',
+ 'HalfCheetah-v2', 'Hopper-v2', 'Walker2d-v2',
+ 'Reacher-v2', 'Swimmer-v2']
+register_benchmark({
+ 'name': 'Mujoco1M',
+ 'description': 'Some small 2D MuJoCo tasks, run for 1M timesteps',
+ 'tasks': [{'env_id': _envid, 'trials': 6, 'num_timesteps': int(1e6)} for _envid in _mujocosmall]
+})
+
+register_benchmark({
+ 'name': 'MujocoWalkers',
+ 'description': 'MuJoCo forward walkers, run for 8M, humanoid 100M',
+ 'tasks': [
+ {'env_id': "Hopper-v1", 'trials': 4, 'num_timesteps': 8 * 1000000},
+ {'env_id': "Walker2d-v1", 'trials': 4, 'num_timesteps': 8 * 1000000},
+ {'env_id': "Humanoid-v1", 'trials': 4, 'num_timesteps': 100 * 1000000},
+ ]
+})
+
+# Bullet
+_bulletsmall = [
+ 'InvertedDoublePendulum', 'InvertedPendulum', 'HalfCheetah', 'Reacher', 'Walker2D', 'Hopper', 'Ant'
+]
+_bulletsmall = [e + 'BulletEnv-v0' for e in _bulletsmall]
+
+register_benchmark({
+ 'name': 'Bullet1M',
+ 'description': '6 mujoco-like tasks from bullet, 1M steps',
+ 'tasks': [{'env_id': e, 'trials': 6, 'num_timesteps': int(1e6)} for e in _bulletsmall]
+})
+
+
+# Roboschool
+
+register_benchmark({
+ 'name': 'Roboschool8M',
+ 'description': 'Small 2D tasks, up to 30 minutes to complete on 8 cores',
+ 'tasks': [
+ {'env_id': "RoboschoolReacher-v1", 'trials': 4, 'num_timesteps': 2 * 1000000},
+ {'env_id': "RoboschoolAnt-v1", 'trials': 4, 'num_timesteps': 8 * 1000000},
+ {'env_id': "RoboschoolHalfCheetah-v1", 'trials': 4, 'num_timesteps': 8 * 1000000},
+ {'env_id': "RoboschoolHopper-v1", 'trials': 4, 'num_timesteps': 8 * 1000000},
+ {'env_id': "RoboschoolWalker2d-v1", 'trials': 4, 'num_timesteps': 8 * 1000000},
+ ]
+})
+register_benchmark({
+ 'name': 'RoboschoolHarder',
+ 'description': 'Test your might!!! Up to 12 hours on 32 cores',
+ 'tasks': [
+ {'env_id': "RoboschoolHumanoid-v1", 'trials': 4, 'num_timesteps': 100 * 1000000},
+ {'env_id': "RoboschoolHumanoidFlagrun-v1", 'trials': 4, 'num_timesteps': 200 * 1000000},
+ {'env_id': "RoboschoolHumanoidFlagrunHarder-v1", 'trials': 4, 'num_timesteps': 400 * 1000000},
+ ]
+})
+
+# Other
+
+_atari50 = [ # actually 47
+ 'Alien', 'Amidar', 'Assault', 'Asterix', 'Asteroids',
+ 'Atlantis', 'BankHeist', 'BattleZone', 'BeamRider', 'Bowling',
+ 'Breakout', 'Centipede', 'ChopperCommand', 'CrazyClimber',
+ 'DemonAttack', 'DoubleDunk', 'Enduro', 'FishingDerby', 'Freeway',
+ 'Frostbite', 'Gopher', 'Gravitar', 'IceHockey', 'Jamesbond',
+ 'Kangaroo', 'Krull', 'KungFuMaster', 'MontezumaRevenge', 'MsPacman',
+ 'NameThisGame', 'Pitfall', 'Pong', 'PrivateEye', 'Qbert',
+ 'RoadRunner', 'Robotank', 'Seaquest', 'SpaceInvaders', 'StarGunner',
+ 'Tennis', 'TimePilot', 'Tutankham', 'UpNDown', 'Venture',
+ 'VideoPinball', 'WizardOfWor', 'Zaxxon',
+]
+
+register_benchmark({
+ 'name': 'Atari50_10M',
+ 'description': '47 Atari games from Mnih et al. (2013), with pixel observations, 10M timesteps',
+ 'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 2, 'num_timesteps': int(10e6)} for _game in _atari50]
+})
+
+# HER DDPG
+
+_fetch_tasks = ['FetchReach-v1', 'FetchPush-v1', 'FetchSlide-v1']
+register_benchmark({
+ 'name': 'Fetch1M',
+ 'description': 'Fetch* benchmarks for 1M timesteps',
+ 'tasks': [{'trials': 6, 'env_id': env_id, 'num_timesteps': int(1e6)} for env_id in _fetch_tasks]
+})
+
diff --git a/baselines/baselines/bench/monitor.py b/baselines/baselines/bench/monitor.py
new file mode 100644
index 0000000000000000000000000000000000000000..e63e71db5c0a1e7d421794e8a64b048a1a6cfc84
--- /dev/null
+++ b/baselines/baselines/bench/monitor.py
@@ -0,0 +1,162 @@
+__all__ = ['Monitor', 'get_monitor_files', 'load_results']
+
+from gym.core import Wrapper
+import time
+from glob import glob
+import csv
+import os.path as osp
+import json
+
+class Monitor(Wrapper):
+ EXT = "monitor.csv"
+ f = None
+
+ def __init__(self, env, filename, allow_early_resets=False, reset_keywords=(), info_keywords=()):
+ Wrapper.__init__(self, env=env)
+ self.tstart = time.time()
+ if filename:
+ self.results_writer = ResultsWriter(filename,
+ header={"t_start": time.time(), 'env_id' : env.spec and env.spec.id},
+ extra_keys=reset_keywords + info_keywords
+ )
+ else:
+ self.results_writer = None
+ self.reset_keywords = reset_keywords
+ self.info_keywords = info_keywords
+ self.allow_early_resets = allow_early_resets
+ self.rewards = None
+ self.needs_reset = True
+ self.episode_rewards = []
+ self.episode_lengths = []
+ self.episode_times = []
+ self.total_steps = 0
+ self.current_reset_info = {} # extra info about the current episode, that was passed in during reset()
+
+ def reset(self, **kwargs):
+ self.reset_state()
+ for k in self.reset_keywords:
+ v = kwargs.get(k)
+ if v is None:
+ raise ValueError('Expected you to pass kwarg %s into reset'%k)
+ self.current_reset_info[k] = v
+ return self.env.reset(**kwargs)
+
+ def reset_state(self):
+ if not self.allow_early_resets and not self.needs_reset:
+ raise RuntimeError("Tried to reset an environment before done. If you want to allow early resets, wrap your env with Monitor(env, path, allow_early_resets=True)")
+ self.rewards = []
+ self.needs_reset = False
+
+
+ def step(self, action):
+ if self.needs_reset:
+ raise RuntimeError("Tried to step environment that needs reset")
+ ob, rew, done, info = self.env.step(action)
+ self.update(ob, rew, done, info)
+ return (ob, rew, done, info)
+
+ def update(self, ob, rew, done, info):
+ self.rewards.append(rew)
+ if done:
+ self.needs_reset = True
+ eprew = sum(self.rewards)
+ eplen = len(self.rewards)
+ epinfo = {"r": round(eprew, 6), "l": eplen, "t": round(time.time() - self.tstart, 6)}
+ for k in self.info_keywords:
+ epinfo[k] = info[k]
+ self.episode_rewards.append(eprew)
+ self.episode_lengths.append(eplen)
+ self.episode_times.append(time.time() - self.tstart)
+ epinfo.update(self.current_reset_info)
+ if self.results_writer:
+ self.results_writer.write_row(epinfo)
+ assert isinstance(info, dict)
+ if isinstance(info, dict):
+ info['episode'] = epinfo
+
+ self.total_steps += 1
+
+ def close(self):
+ if self.f is not None:
+ self.f.close()
+
+ def get_total_steps(self):
+ return self.total_steps
+
+ def get_episode_rewards(self):
+ return self.episode_rewards
+
+ def get_episode_lengths(self):
+ return self.episode_lengths
+
+ def get_episode_times(self):
+ return self.episode_times
+
+class LoadMonitorResultsError(Exception):
+ pass
+
+
+class ResultsWriter(object):
+ def __init__(self, filename, header='', extra_keys=()):
+ self.extra_keys = extra_keys
+ assert filename is not None
+ if not filename.endswith(Monitor.EXT):
+ if osp.isdir(filename):
+ filename = osp.join(filename, Monitor.EXT)
+ else:
+ filename = filename + "." + Monitor.EXT
+ self.f = open(filename, "wt")
+ if isinstance(header, dict):
+ header = '# {} \n'.format(json.dumps(header))
+ self.f.write(header)
+ self.logger = csv.DictWriter(self.f, fieldnames=('r', 'l', 't')+tuple(extra_keys))
+ self.logger.writeheader()
+ self.f.flush()
+
+ def write_row(self, epinfo):
+ if self.logger:
+ self.logger.writerow(epinfo)
+ self.f.flush()
+
+
+def get_monitor_files(dir):
+ return glob(osp.join(dir, "*" + Monitor.EXT))
+
+def load_results(dir):
+ import pandas
+ monitor_files = (
+ glob(osp.join(dir, "*monitor.json")) +
+ glob(osp.join(dir, "*monitor.csv"))) # get both csv and (old) json files
+ if not monitor_files:
+ raise LoadMonitorResultsError("no monitor files of the form *%s found in %s" % (Monitor.EXT, dir))
+ dfs = []
+ headers = []
+ for fname in monitor_files:
+ with open(fname, 'rt') as fh:
+ if fname.endswith('csv'):
+ firstline = fh.readline()
+ if not firstline:
+ continue
+ assert firstline[0] == '#'
+ header = json.loads(firstline[1:])
+ df = pandas.read_csv(fh, index_col=None)
+ headers.append(header)
+ elif fname.endswith('json'): # Deprecated json format
+ episodes = []
+ lines = fh.readlines()
+ header = json.loads(lines[0])
+ headers.append(header)
+ for line in lines[1:]:
+ episode = json.loads(line)
+ episodes.append(episode)
+ df = pandas.DataFrame(episodes)
+ else:
+ assert 0, 'unreachable'
+ df['t'] += header['t_start']
+ dfs.append(df)
+ df = pandas.concat(dfs)
+ df.sort_values('t', inplace=True)
+ df.reset_index(inplace=True)
+ df['t'] -= min(header['t_start'] for header in headers)
+ df.headers = headers # HACK to preserve backwards compatibility
+ return df
diff --git a/baselines/baselines/common/__init__.py b/baselines/baselines/common/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0834b3649275b3b0607acde57f83983155c54ecc
--- /dev/null
+++ b/baselines/baselines/common/__init__.py
@@ -0,0 +1,5 @@
+# flake8: noqa F403
+from baselines.common.console_util import *
+from baselines.common.dataset import Dataset
+from baselines.common.math_util import *
+from baselines.common.misc_util import *
diff --git a/baselines/baselines/common/atari_wrappers.py b/baselines/baselines/common/atari_wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..8be0a2f2542c5d88e3638cf068502b1df58c862b
--- /dev/null
+++ b/baselines/baselines/common/atari_wrappers.py
@@ -0,0 +1,290 @@
+import numpy as np
+import os
+os.environ.setdefault('PATH', '')
+from collections import deque
+import gym
+from gym import spaces
+import cv2
+cv2.ocl.setUseOpenCL(False)
+from .wrappers import TimeLimit
+
+
+class NoopResetEnv(gym.Wrapper):
+ def __init__(self, env, noop_max=30):
+ """Sample initial states by taking random number of no-ops on reset.
+ No-op is assumed to be action 0.
+ """
+ gym.Wrapper.__init__(self, env)
+ self.noop_max = noop_max
+ self.override_num_noops = None
+ self.noop_action = 0
+ assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
+
+ def reset(self, **kwargs):
+ """ Do no-op action for a number of steps in [1, noop_max]."""
+ self.env.reset(**kwargs)
+ if self.override_num_noops is not None:
+ noops = self.override_num_noops
+ else:
+ noops = self.unwrapped.np_random.randint(1, self.noop_max + 1) #pylint: disable=E1101
+ assert noops > 0
+ obs = None
+ for _ in range(noops):
+ obs, _, done, _ = self.env.step(self.noop_action)
+ if done:
+ obs = self.env.reset(**kwargs)
+ return obs
+
+ def step(self, ac):
+ return self.env.step(ac)
+
+class FireResetEnv(gym.Wrapper):
+ def __init__(self, env):
+ """Take action on reset for environments that are fixed until firing."""
+ gym.Wrapper.__init__(self, env)
+ assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
+ assert len(env.unwrapped.get_action_meanings()) >= 3
+
+ def reset(self, **kwargs):
+ self.env.reset(**kwargs)
+ obs, _, done, _ = self.env.step(1)
+ if done:
+ self.env.reset(**kwargs)
+ obs, _, done, _ = self.env.step(2)
+ if done:
+ self.env.reset(**kwargs)
+ return obs
+
+ def step(self, ac):
+ return self.env.step(ac)
+
+class EpisodicLifeEnv(gym.Wrapper):
+ def __init__(self, env):
+ """Make end-of-life == end-of-episode, but only reset on true game over.
+ Done by DeepMind for the DQN and co. since it helps value estimation.
+ """
+ gym.Wrapper.__init__(self, env)
+ self.lives = 0
+ self.was_real_done = True
+
+ def step(self, action):
+ obs, reward, done, info = self.env.step(action)
+ self.was_real_done = done
+ # check current lives, make loss of life terminal,
+ # then update lives to handle bonus lives
+ lives = self.env.unwrapped.ale.lives()
+ if lives < self.lives and lives > 0:
+ # for Qbert sometimes we stay in lives == 0 condition for a few frames
+ # so it's important to keep lives > 0, so that we only reset once
+ # the environment advertises done.
+ done = True
+ self.lives = lives
+ return obs, reward, done, info
+
+ def reset(self, **kwargs):
+ """Reset only when lives are exhausted.
+ This way all states are still reachable even though lives are episodic,
+ and the learner need not know about any of this behind-the-scenes.
+ """
+ if self.was_real_done:
+ obs = self.env.reset(**kwargs)
+ else:
+ # no-op step to advance from terminal/lost life state
+ obs, _, _, _ = self.env.step(0)
+ self.lives = self.env.unwrapped.ale.lives()
+ return obs
+
+class MaxAndSkipEnv(gym.Wrapper):
+ def __init__(self, env, skip=4):
+ """Return only every `skip`-th frame"""
+ gym.Wrapper.__init__(self, env)
+ # most recent raw observations (for max pooling across time steps)
+ self._obs_buffer = np.zeros((2,)+env.observation_space.shape, dtype=np.uint8)
+ self._skip = skip
+
+ def step(self, action):
+ """Repeat action, sum reward, and max over last observations."""
+ total_reward = 0.0
+ done = None
+ for i in range(self._skip):
+ obs, reward, done, info = self.env.step(action)
+ if i == self._skip - 2: self._obs_buffer[0] = obs
+ if i == self._skip - 1: self._obs_buffer[1] = obs
+ total_reward += reward
+ if done:
+ break
+ # Note that the observation on the done=True frame
+ # doesn't matter
+ max_frame = self._obs_buffer.max(axis=0)
+
+ return max_frame, total_reward, done, info
+
+ def reset(self, **kwargs):
+ return self.env.reset(**kwargs)
+
+class ClipRewardEnv(gym.RewardWrapper):
+ def __init__(self, env):
+ gym.RewardWrapper.__init__(self, env)
+
+ def reward(self, reward):
+ """Bin reward to {+1, 0, -1} by its sign."""
+ return np.sign(reward)
+
+
+class WarpFrame(gym.ObservationWrapper):
+ def __init__(self, env, width=84, height=84, grayscale=True, dict_space_key=None):
+ """
+ Warp frames to 84x84 as done in the Nature paper and later work.
+
+ If the environment uses dictionary observations, `dict_space_key` can be specified which indicates which
+ observation should be warped.
+ """
+ super().__init__(env)
+ self._width = width
+ self._height = height
+ self._grayscale = grayscale
+ self._key = dict_space_key
+ if self._grayscale:
+ num_colors = 1
+ else:
+ num_colors = 3
+
+ new_space = gym.spaces.Box(
+ low=0,
+ high=255,
+ shape=(self._height, self._width, num_colors),
+ dtype=np.uint8,
+ )
+ if self._key is None:
+ original_space = self.observation_space
+ self.observation_space = new_space
+ else:
+ original_space = self.observation_space.spaces[self._key]
+ self.observation_space.spaces[self._key] = new_space
+ assert original_space.dtype == np.uint8 and len(original_space.shape) == 3
+
+ def observation(self, obs):
+ if self._key is None:
+ frame = obs
+ else:
+ frame = obs[self._key]
+
+ if self._grayscale:
+ frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+ frame = cv2.resize(
+ frame, (self._width, self._height), interpolation=cv2.INTER_AREA
+ )
+ if self._grayscale:
+ frame = np.expand_dims(frame, -1)
+
+ if self._key is None:
+ obs = frame
+ else:
+ obs = obs.copy()
+ obs[self._key] = frame
+ return obs
+
+
+class FrameStack(gym.Wrapper):
+ def __init__(self, env, k):
+ """Stack k last frames.
+
+ Returns lazy array, which is much more memory efficient.
+
+ See Also
+ --------
+ baselines.common.atari_wrappers.LazyFrames
+ """
+ gym.Wrapper.__init__(self, env)
+ self.k = k
+ self.frames = deque([], maxlen=k)
+ shp = env.observation_space.shape
+ self.observation_space = spaces.Box(low=0, high=255, shape=(shp[:-1] + (shp[-1] * k,)), dtype=env.observation_space.dtype)
+
+ def reset(self):
+ ob = self.env.reset()
+ for _ in range(self.k):
+ self.frames.append(ob)
+ return self._get_ob()
+
+ def step(self, action):
+ ob, reward, done, info = self.env.step(action)
+ self.frames.append(ob)
+ return self._get_ob(), reward, done, info
+
+ def _get_ob(self):
+ assert len(self.frames) == self.k
+ return LazyFrames(list(self.frames))
+
+class ScaledFloatFrame(gym.ObservationWrapper):
+ def __init__(self, env):
+ gym.ObservationWrapper.__init__(self, env)
+ self.observation_space = gym.spaces.Box(low=0, high=1, shape=env.observation_space.shape, dtype=np.float32)
+
+ def observation(self, observation):
+ # careful! This undoes the memory optimization, use
+ # with smaller replay buffers only.
+ return np.array(observation).astype(np.float32) / 255.0
+
+class LazyFrames(object):
+ def __init__(self, frames):
+ """This object ensures that common frames between the observations are only stored once.
+ It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
+ buffers.
+
+ This object should only be converted to numpy array before being passed to the model.
+
+ You'd not believe how complex the previous solution was."""
+ self._frames = frames
+ self._out = None
+
+ def _force(self):
+ if self._out is None:
+ self._out = np.concatenate(self._frames, axis=-1)
+ self._frames = None
+ return self._out
+
+ def __array__(self, dtype=None):
+ out = self._force()
+ if dtype is not None:
+ out = out.astype(dtype)
+ return out
+
+ def __len__(self):
+ return len(self._force())
+
+ def __getitem__(self, i):
+ return self._force()[i]
+
+ def count(self):
+ frames = self._force()
+ return frames.shape[frames.ndim - 1]
+
+ def frame(self, i):
+ return self._force()[..., i]
+
+def make_atari(env_id, max_episode_steps=None):
+ env = gym.make(env_id)
+ assert 'NoFrameskip' in env.spec.id
+ env = NoopResetEnv(env, noop_max=30)
+ env = MaxAndSkipEnv(env, skip=4)
+ if max_episode_steps is not None:
+ env = TimeLimit(env, max_episode_steps=max_episode_steps)
+ return env
+
+def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):
+ """Configure environment for DeepMind-style Atari.
+ """
+ if episode_life:
+ env = EpisodicLifeEnv(env)
+ if 'FIRE' in env.unwrapped.get_action_meanings():
+ env = FireResetEnv(env)
+ env = WarpFrame(env)
+ if scale:
+ env = ScaledFloatFrame(env)
+ if clip_rewards:
+ env = ClipRewardEnv(env)
+ if frame_stack:
+ env = FrameStack(env, 4)
+ return env
+
diff --git a/baselines/baselines/common/cg.py b/baselines/baselines/common/cg.py
new file mode 100644
index 0000000000000000000000000000000000000000..52ca49dc865799c69c746b11e5f8813ba044ef8c
--- /dev/null
+++ b/baselines/baselines/common/cg.py
@@ -0,0 +1,34 @@
+import numpy as np
+def cg(f_Ax, b, cg_iters=10, callback=None, verbose=False, residual_tol=1e-10):
+ """
+ Demmel p 312
+ """
+ p = b.copy()
+ r = b.copy()
+ x = np.zeros_like(b)
+ rdotr = r.dot(r)
+
+ fmtstr = "%10i %10.3g %10.3g"
+ titlestr = "%10s %10s %10s"
+ if verbose: print(titlestr % ("iter", "residual norm", "soln norm"))
+
+ for i in range(cg_iters):
+ if callback is not None:
+ callback(x)
+ if verbose: print(fmtstr % (i, rdotr, np.linalg.norm(x)))
+ z = f_Ax(p)
+ v = rdotr / p.dot(z)
+ x += v*p
+ r -= v*z
+ newrdotr = r.dot(r)
+ mu = newrdotr/rdotr
+ p = r + mu*p
+
+ rdotr = newrdotr
+ if rdotr < residual_tol:
+ break
+
+ if callback is not None:
+ callback(x)
+ if verbose: print(fmtstr % (i+1, rdotr, np.linalg.norm(x))) # pylint: disable=W0631
+ return x
diff --git a/baselines/baselines/common/cmd_util.py b/baselines/baselines/common/cmd_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb04dd05a2c36865b8322ff44d4e260c4b59a549
--- /dev/null
+++ b/baselines/baselines/common/cmd_util.py
@@ -0,0 +1,186 @@
+"""
+Helpers for scripts like run_atari.py.
+"""
+
+import os
+try:
+ from mpi4py import MPI
+except ImportError:
+ MPI = None
+
+import gym
+from gym.wrappers import FlattenDictWrapper
+from baselines import logger
+from baselines.bench import Monitor
+from baselines.common import set_global_seeds
+from baselines.common.atari_wrappers import make_atari, wrap_deepmind
+from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
+from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
+from baselines.common import retro_wrappers
+
+def make_vec_env(env_id, env_type, num_env, seed,
+ wrapper_kwargs=None,
+ start_index=0,
+ reward_scale=1.0,
+ flatten_dict_observations=True,
+ gamestate=None):
+ """
+ Create a wrapped, monitored SubprocVecEnv for Atari and MuJoCo.
+ """
+ wrapper_kwargs = wrapper_kwargs or {}
+ mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
+ seed = seed + 10000 * mpi_rank if seed is not None else None
+ logger_dir = logger.get_dir()
+ def make_thunk(rank):
+ return lambda: make_env(
+ env_id=env_id,
+ env_type=env_type,
+ mpi_rank=mpi_rank,
+ subrank=rank,
+ seed=seed,
+ reward_scale=reward_scale,
+ gamestate=gamestate,
+ flatten_dict_observations=flatten_dict_observations,
+ wrapper_kwargs=wrapper_kwargs,
+ logger_dir=logger_dir
+ )
+
+ set_global_seeds(seed)
+ if num_env > 1:
+ return SubprocVecEnv([make_thunk(i + start_index) for i in range(num_env)])
+ else:
+ return DummyVecEnv([make_thunk(start_index)])
+
+
+def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.0, gamestate=None, flatten_dict_observations=True, wrapper_kwargs=None, logger_dir=None):
+ wrapper_kwargs = wrapper_kwargs or {}
+ if env_type == 'atari':
+ env = make_atari(env_id)
+ elif env_type == 'retro':
+ import retro
+ gamestate = gamestate or retro.State.DEFAULT
+ env = retro_wrappers.make_retro(game=env_id, max_episode_steps=10000, use_restricted_actions=retro.Actions.DISCRETE, state=gamestate)
+ else:
+ env = gym.make(env_id)
+
+ if flatten_dict_observations and isinstance(env.observation_space, gym.spaces.Dict):
+ keys = env.observation_space.spaces.keys()
+ env = gym.wrappers.FlattenDictWrapper(env, dict_keys=list(keys))
+
+ env.seed(seed + subrank if seed is not None else None)
+ env = Monitor(env,
+ logger_dir and os.path.join(logger_dir, str(mpi_rank) + '.' + str(subrank)),
+ allow_early_resets=True)
+
+ if env_type == 'atari':
+ env = wrap_deepmind(env, **wrapper_kwargs)
+ elif env_type == 'retro':
+ if 'frame_stack' not in wrapper_kwargs:
+ wrapper_kwargs['frame_stack'] = 1
+ env = retro_wrappers.wrap_deepmind_retro(env, **wrapper_kwargs)
+
+ if reward_scale != 1:
+ env = retro_wrappers.RewardScaler(env, reward_scale)
+
+ return env
+
+
+def make_mujoco_env(env_id, seed, reward_scale=1.0):
+ """
+ Create a wrapped, monitored gym.Env for MuJoCo.
+ """
+ rank = MPI.COMM_WORLD.Get_rank()
+ myseed = seed + 1000 * rank if seed is not None else None
+ set_global_seeds(myseed)
+ env = gym.make(env_id)
+ logger_path = None if logger.get_dir() is None else os.path.join(logger.get_dir(), str(rank))
+ env = Monitor(env, logger_path, allow_early_resets=True)
+ env.seed(seed)
+ if reward_scale != 1.0:
+ from baselines.common.retro_wrappers import RewardScaler
+ env = RewardScaler(env, reward_scale)
+ return env
+
+def make_robotics_env(env_id, seed, rank=0):
+ """
+ Create a wrapped, monitored gym.Env for MuJoCo.
+ """
+ set_global_seeds(seed)
+ env = gym.make(env_id)
+ env = FlattenDictWrapper(env, ['observation', 'desired_goal'])
+ env = Monitor(
+ env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)),
+ info_keywords=('is_success',))
+ env.seed(seed)
+ return env
+
+def arg_parser():
+ """
+ Create an empty argparse.ArgumentParser.
+ """
+ import argparse
+ return argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+def atari_arg_parser():
+ """
+ Create an argparse.ArgumentParser for run_atari.py.
+ """
+ print('Obsolete - use common_arg_parser instead')
+ return common_arg_parser()
+
+def mujoco_arg_parser():
+ print('Obsolete - use common_arg_parser instead')
+ return common_arg_parser()
+
+def common_arg_parser():
+ """
+ Create an argparse.ArgumentParser for run_mujoco.py.
+ """
+ parser = arg_parser()
+ parser.add_argument('--env', help='environment ID', type=str, default='Reacher-v2')
+ parser.add_argument('--env_type', help='type of environment, used when the environment type cannot be automatically determined', type=str)
+ parser.add_argument('--seed', help='RNG seed', type=int, default=None)
+ parser.add_argument('--alg', help='Algorithm', type=str, default='ppo2')
+ parser.add_argument('--num_timesteps', type=float, default=1e6),
+ parser.add_argument('--network', help='network type (mlp, cnn, lstm, cnn_lstm, conv_only)', default=None)
+ parser.add_argument('--gamestate', help='game state to load (so far only used in retro games)', default=None)
+ parser.add_argument('--num_env', help='Number of environment copies being run in parallel. When not specified, set to number of cpus for Atari, and to 1 for Mujoco', default=None, type=int)
+ parser.add_argument('--reward_scale', help='Reward scale factor. Default: 1.0', default=1.0, type=float)
+ parser.add_argument('--save_path', help='Path to save trained model to', default=None, type=str)
+ parser.add_argument('--save_video_interval', help='Save video every x steps (0 = disabled)', default=0, type=int)
+ parser.add_argument('--save_video_length', help='Length of recorded video. Default: 200', default=200, type=int)
+ parser.add_argument('--log_path', help='Directory to save learning curve data.', default=None, type=str)
+ parser.add_argument('--play', default=False, action='store_true')
+ return parser
+
+def robotics_arg_parser():
+ """
+ Create an argparse.ArgumentParser for run_mujoco.py.
+ """
+ parser = arg_parser()
+ parser.add_argument('--env', help='environment ID', type=str, default='FetchReach-v0')
+ parser.add_argument('--seed', help='RNG seed', type=int, default=None)
+ parser.add_argument('--num-timesteps', type=int, default=int(1e6))
+ return parser
+
+
+def parse_unknown_args(args):
+ """
+ Parse arguments not consumed by arg parser into a dictionary
+ """
+ retval = {}
+ preceded_by_key = False
+ for arg in args:
+ if arg.startswith('--'):
+ if '=' in arg:
+ key = arg.split('=')[0][2:]
+ value = arg.split('=')[1]
+ retval[key] = value
+ else:
+ key = arg[2:]
+ preceded_by_key = True
+ elif preceded_by_key:
+ retval[key] = arg
+ preceded_by_key = False
+
+ return retval
diff --git a/baselines/baselines/common/console_util.py b/baselines/baselines/common/console_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b011c59750ca38a3fc96f73735b61f72c04db02
--- /dev/null
+++ b/baselines/baselines/common/console_util.py
@@ -0,0 +1,80 @@
+from __future__ import print_function
+from contextlib import contextmanager
+import numpy as np
+import time
+import shlex
+import subprocess
+
+# ================================================================
+# Misc
+# ================================================================
+
+def fmt_row(width, row, header=False):
+ out = " | ".join(fmt_item(x, width) for x in row)
+ if header: out = out + "\n" + "-"*len(out)
+ return out
+
+def fmt_item(x, l):
+ if isinstance(x, np.ndarray):
+ assert x.ndim==0
+ x = x.item()
+ if isinstance(x, (float, np.float32, np.float64)):
+ v = abs(x)
+ if (v < 1e-4 or v > 1e+4) and v > 0:
+ rep = "%7.2e" % x
+ else:
+ rep = "%7.5f" % x
+ else: rep = str(x)
+ return " "*(l - len(rep)) + rep
+
+color2num = dict(
+ gray=30,
+ red=31,
+ green=32,
+ yellow=33,
+ blue=34,
+ magenta=35,
+ cyan=36,
+ white=37,
+ crimson=38
+)
+
+def colorize(string, color='green', bold=False, highlight=False):
+ attr = []
+ num = color2num[color]
+ if highlight: num += 10
+ attr.append(str(num))
+ if bold: attr.append('1')
+ return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string)
+
+def print_cmd(cmd, dry=False):
+ if isinstance(cmd, str): # for shell=True
+ pass
+ else:
+ cmd = ' '.join(shlex.quote(arg) for arg in cmd)
+ print(colorize(('CMD: ' if not dry else 'DRY: ') + cmd))
+
+
+def get_git_commit(cwd=None):
+ return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], cwd=cwd).decode('utf8')
+
+def get_git_commit_message(cwd=None):
+ return subprocess.check_output(['git', 'show', '-s', '--format=%B', 'HEAD'], cwd=cwd).decode('utf8')
+
+def ccap(cmd, dry=False, env=None, **kwargs):
+ print_cmd(cmd, dry)
+ if not dry:
+ subprocess.check_call(cmd, env=env, **kwargs)
+
+
+MESSAGE_DEPTH = 0
+
+@contextmanager
+def timed(msg):
+ global MESSAGE_DEPTH #pylint: disable=W0603
+ print(colorize('\t'*MESSAGE_DEPTH + '=: ' + msg, color='magenta'))
+ tstart = time.time()
+ MESSAGE_DEPTH += 1
+ yield
+ MESSAGE_DEPTH -= 1
+ print(colorize('\t'*MESSAGE_DEPTH + "done in %.3f seconds"%(time.time() - tstart), color='magenta'))
diff --git a/baselines/baselines/common/dataset.py b/baselines/baselines/common/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..41a38c8af68cd4c86cc01e862e8a4193b98c7e1a
--- /dev/null
+++ b/baselines/baselines/common/dataset.py
@@ -0,0 +1,60 @@
+import numpy as np
+
+class Dataset(object):
+ def __init__(self, data_map, deterministic=False, shuffle=True):
+ self.data_map = data_map
+ self.deterministic = deterministic
+ self.enable_shuffle = shuffle
+ self.n = next(iter(data_map.values())).shape[0]
+ self._next_id = 0
+ self.shuffle()
+
+ def shuffle(self):
+ if self.deterministic:
+ return
+ perm = np.arange(self.n)
+ np.random.shuffle(perm)
+
+ for key in self.data_map:
+ self.data_map[key] = self.data_map[key][perm]
+
+ self._next_id = 0
+
+ def next_batch(self, batch_size):
+ if self._next_id >= self.n and self.enable_shuffle:
+ self.shuffle()
+
+ cur_id = self._next_id
+ cur_batch_size = min(batch_size, self.n - self._next_id)
+ self._next_id += cur_batch_size
+
+ data_map = dict()
+ for key in self.data_map:
+ data_map[key] = self.data_map[key][cur_id:cur_id+cur_batch_size]
+ return data_map
+
+ def iterate_once(self, batch_size):
+ if self.enable_shuffle: self.shuffle()
+
+ while self._next_id <= self.n - batch_size:
+ yield self.next_batch(batch_size)
+ self._next_id = 0
+
+ def subset(self, num_elements, deterministic=True):
+ data_map = dict()
+ for key in self.data_map:
+ data_map[key] = self.data_map[key][:num_elements]
+ return Dataset(data_map, deterministic)
+
+
+def iterbatches(arrays, *, num_batches=None, batch_size=None, shuffle=True, include_final_partial_batch=True):
+ assert (num_batches is None) != (batch_size is None), 'Provide num_batches or batch_size, but not both'
+ arrays = tuple(map(np.asarray, arrays))
+ n = arrays[0].shape[0]
+ assert all(a.shape[0] == n for a in arrays[1:])
+ inds = np.arange(n)
+ if shuffle: np.random.shuffle(inds)
+ sections = np.arange(0, n, batch_size)[1:] if num_batches is None else num_batches
+ for batch_inds in np.array_split(inds, sections):
+ if include_final_partial_batch or len(batch_inds) == batch_size:
+ yield tuple(a[batch_inds] for a in arrays)
diff --git a/baselines/baselines/common/distributions.py b/baselines/baselines/common/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..c58db7f28841ec1ebb2dd432984a6d952589ebfb
--- /dev/null
+++ b/baselines/baselines/common/distributions.py
@@ -0,0 +1,186 @@
+import tensorflow as tf
+import numpy as np
+from baselines.a2c.utils import fc
+
+class Pd(object):
+ """
+ A particular probability distribution
+ """
+ def flatparam(self):
+ raise NotImplementedError
+ def mode(self):
+ raise NotImplementedError
+ def neglogp(self, x):
+ # Usually it's easier to define the negative logprob
+ raise NotImplementedError
+ def kl(self, other):
+ raise NotImplementedError
+ def entropy(self):
+ raise NotImplementedError
+ def sample(self):
+ raise NotImplementedError
+ def logp(self, x):
+ return - self.neglogp(x)
+ def get_shape(self):
+ return self.flatparam().shape
+ @property
+ def shape(self):
+ return self.get_shape()
+ def __getitem__(self, idx):
+ return self.__class__(self.flatparam()[idx])
+
+class PdType(tf.Module):
+ """
+ Parametrized family of probability distributions
+ """
+ def pdclass(self):
+ raise NotImplementedError
+ def pdfromflat(self, flat):
+ return self.pdclass()(flat)
+ def pdfromlatent(self, latent_vector):
+ raise NotImplementedError
+ def param_shape(self):
+ raise NotImplementedError
+ def sample_shape(self):
+ raise NotImplementedError
+ def sample_dtype(self):
+ raise NotImplementedError
+
+ def __eq__(self, other):
+ return (type(self) == type(other)) and (self.__dict__ == other.__dict__)
+
+class CategoricalPdType(PdType):
+ def __init__(self, latent_shape, ncat, init_scale=1.0, init_bias=0.0):
+ self.ncat = ncat
+ self.matching_fc = _matching_fc(latent_shape, 'pi', self.ncat, init_scale=init_scale, init_bias=init_bias)
+
+ def pdclass(self):
+ return CategoricalPd
+ def pdfromlatent(self, latent_vector):
+ pdparam = self.matching_fc(latent_vector)
+ return self.pdfromflat(pdparam), pdparam
+
+ def param_shape(self):
+ return [self.ncat]
+ def sample_shape(self):
+ return []
+ def sample_dtype(self):
+ return tf.int32
+
+class DiagGaussianPdType(PdType):
+ def __init__(self, latent_shape, size, init_scale=1.0, init_bias=0.0):
+ self.size = size
+ self.matching_fc = _matching_fc(latent_shape, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
+ self.logstd = tf.Variable(np.zeros((1, self.size)), name='pi/logstd', dtype=tf.float32)
+
+ def pdclass(self):
+ return DiagGaussianPd
+
+ def pdfromlatent(self, latent_vector):
+ mean = self.matching_fc(latent_vector)
+ pdparam = tf.concat([mean, mean * 0.0 + self.logstd], axis=1)
+ return self.pdfromflat(pdparam), mean
+
+ def param_shape(self):
+ return [2*self.size]
+ def sample_shape(self):
+ return [self.size]
+ def sample_dtype(self):
+ return tf.float32
+
+
+class CategoricalPd(Pd):
+ def __init__(self, logits):
+ self.logits = logits
+ def flatparam(self):
+ return self.logits
+ def mode(self):
+ return tf.argmax(self.logits, axis=-1)
+
+ @property
+ def mean(self):
+ return tf.nn.softmax(self.logits)
+
+ def neglogp(self, x):
+ # return tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits, labels=x)
+ # Note: we can't use sparse_softmax_cross_entropy_with_logits because
+ # the implementation does not allow second-order derivatives...
+ if x.dtype in {tf.uint8, tf.int32, tf.int64}:
+ # one-hot encoding
+ x_shape_list = x.shape.as_list()
+ logits_shape_list = self.logits.get_shape().as_list()[:-1]
+ for xs, ls in zip(x_shape_list, logits_shape_list):
+ if xs is not None and ls is not None:
+ assert xs == ls, 'shape mismatch: {} in x vs {} in logits'.format(xs, ls)
+
+ x = tf.one_hot(x, self.logits.get_shape().as_list()[-1])
+ else:
+ # already encoded
+ print('logits is {}'.format(self.logits))
+ assert x.shape.as_list() == self.logits.shape.as_list()
+
+ return tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=x)
+
+ def kl(self, other):
+ a0 = self.logits - tf.reduce_max(self.logits, axis=-1, keepdims=True)
+ a1 = other.logits - tf.reduce_max(other.logits, axis=-1, keepdims=True)
+ ea0 = tf.exp(a0)
+ ea1 = tf.exp(a1)
+ z0 = tf.reduce_sum(ea0, axis=-1, keepdims=True)
+ z1 = tf.reduce_sum(ea1, axis=-1, keepdims=True)
+ p0 = ea0 / z0
+ return tf.reduce_sum(p0 * (a0 - tf.math.log(z0) - a1 + tf.math.log(z1)), axis=-1)
+ def entropy(self):
+ a0 = self.logits - tf.reduce_max(self.logits, axis=-1, keepdims=True)
+ ea0 = tf.exp(a0)
+ z0 = tf.reduce_sum(ea0, axis=-1, keepdims=True)
+ p0 = ea0 / z0
+ return tf.reduce_sum(p0 * (tf.math.log(z0) - a0), axis=-1)
+ def sample(self):
+ u = tf.random.uniform(tf.shape(self.logits), dtype=self.logits.dtype, seed=0)
+ return tf.argmax(self.logits - tf.math.log(-tf.math.log(u)), axis=-1)
+ @classmethod
+ def fromflat(cls, flat):
+ return cls(flat)
+
+class DiagGaussianPd(Pd):
+ def __init__(self, flat):
+ self.flat = flat
+ mean, logstd = tf.split(axis=len(flat.shape)-1, num_or_size_splits=2, value=flat)
+ self.mean = mean
+ self.logstd = logstd
+ self.std = tf.exp(logstd)
+ def flatparam(self):
+ return self.flat
+ def mode(self):
+ return self.mean
+ def neglogp(self, x):
+ return 0.5 * tf.reduce_sum(tf.square((x - self.mean) / self.std), axis=-1) \
+ + 0.5 * np.log(2.0 * np.pi) * tf.cast(tf.shape(x)[-1], dtype=tf.float32) \
+ + tf.reduce_sum(self.logstd, axis=-1)
+ def kl(self, other):
+ assert isinstance(other, DiagGaussianPd)
+ return tf.reduce_sum(other.logstd - self.logstd + (tf.square(self.std) + tf.square(self.mean - other.mean)) / (2.0 * tf.square(other.std)) - 0.5, axis=-1)
+ def entropy(self):
+ return tf.reduce_sum(self.logstd + .5 * np.log(2.0 * np.pi * np.e), axis=-1)
+ def sample(self):
+ return self.mean + self.std * tf.random.normal(tf.shape(self.mean))
+ @classmethod
+ def fromflat(cls, flat):
+ return cls(flat)
+
+def make_pdtype(latent_shape, ac_space, init_scale=1.0):
+ from gym import spaces
+ if isinstance(ac_space, spaces.Box):
+ assert len(ac_space.shape) == 1
+ return DiagGaussianPdType(latent_shape, ac_space.shape[0], init_scale)
+ elif isinstance(ac_space, spaces.Discrete):
+ return CategoricalPdType(latent_shape, ac_space.n, init_scale)
+ else:
+ raise ValueError('No implementation for {}'.format(ac_space))
+
+def _matching_fc(tensor_shape, name, size, init_scale, init_bias):
+ if tensor_shape[-1] == size:
+ return lambda x: x
+ else:
+ return fc(tensor_shape, name, size, init_scale=init_scale, init_bias=init_bias)
diff --git a/baselines/baselines/common/math_util.py b/baselines/baselines/common/math_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..461bdb79908696e1d7b1b811f578d7216c883b71
--- /dev/null
+++ b/baselines/baselines/common/math_util.py
@@ -0,0 +1,85 @@
+import numpy as np
+import scipy.signal
+
+
+def discount(x, gamma):
+ """
+ computes discounted sums along 0th dimension of x.
+
+ inputs
+ ------
+ x: ndarray
+ gamma: float
+
+ outputs
+ -------
+ y: ndarray with same shape as x, satisfying
+
+ y[t] = x[t] + gamma*x[t+1] + gamma^2*x[t+2] + ... + gamma^k x[t+k],
+ where k = len(x) - t - 1
+
+ """
+ assert x.ndim >= 1
+ return scipy.signal.lfilter([1],[1,-gamma],x[::-1], axis=0)[::-1]
+
+def explained_variance(ypred,y):
+ """
+ Computes fraction of variance that ypred explains about y.
+ Returns 1 - Var[y-ypred] / Var[y]
+
+ interpretation:
+ ev=0 => might as well have predicted zero
+ ev=1 => perfect prediction
+ ev<0 => worse than just predicting zero
+
+ """
+ assert y.ndim == 1 and ypred.ndim == 1
+ vary = np.var(y)
+ return np.nan if vary==0 else 1 - np.var(y-ypred)/vary
+
+def explained_variance_2d(ypred, y):
+ assert y.ndim == 2 and ypred.ndim == 2
+ vary = np.var(y, axis=0)
+ out = 1 - np.var(y-ypred)/vary
+ out[vary < 1e-10] = 0
+ return out
+
+def ncc(ypred, y):
+ return np.corrcoef(ypred, y)[1,0]
+
+def flatten_arrays(arrs):
+ return np.concatenate([arr.flat for arr in arrs])
+
+def unflatten_vector(vec, shapes):
+ i=0
+ arrs = []
+ for shape in shapes:
+ size = np.prod(shape)
+ arr = vec[i:i+size].reshape(shape)
+ arrs.append(arr)
+ i += size
+ return arrs
+
+def discount_with_boundaries(X, New, gamma):
+ """
+ X: 2d array of floats, time x features
+ New: 2d array of bools, indicating when a new episode has started
+ """
+ Y = np.zeros_like(X)
+ T = X.shape[0]
+ Y[T-1] = X[T-1]
+ for t in range(T-2, -1, -1):
+ Y[t] = X[t] + gamma * Y[t+1] * (1 - New[t+1])
+ return Y
+
+def test_discount_with_boundaries():
+ gamma=0.9
+ x = np.array([1.0, 2.0, 3.0, 4.0], 'float32')
+ starts = [1.0, 0.0, 0.0, 1.0]
+ y = discount_with_boundaries(x, starts, gamma)
+ assert np.allclose(y, [
+ 1 + gamma * 2 + gamma**2 * 3,
+ 2 + gamma * 3,
+ 3,
+ 4
+ ])
diff --git a/baselines/baselines/common/misc_util.py b/baselines/baselines/common/misc_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..96ab3cad945fa35c82bf8ec3e70c41502acab868
--- /dev/null
+++ b/baselines/baselines/common/misc_util.py
@@ -0,0 +1,243 @@
+import gym
+import numpy as np
+import os
+import pickle
+import random
+import tempfile
+import zipfile
+
+
+def zipsame(*seqs):
+ L = len(seqs[0])
+ assert all(len(seq) == L for seq in seqs[1:])
+ return zip(*seqs)
+
+
+class EzPickle(object):
+ """Objects that are pickled and unpickled via their constructor
+ arguments.
+
+ Example usage:
+
+ class Dog(Animal, EzPickle):
+ def __init__(self, furcolor, tailkind="bushy"):
+ Animal.__init__()
+ EzPickle.__init__(furcolor, tailkind)
+ ...
+
+ When this object is unpickled, a new Dog will be constructed by passing the provided
+ furcolor and tailkind into the constructor. However, philosophers are still not sure
+ whether it is still the same dog.
+
+ This is generally needed only for environments which wrap C/C++ code, such as MuJoCo
+ and Atari.
+ """
+
+ def __init__(self, *args, **kwargs):
+ self._ezpickle_args = args
+ self._ezpickle_kwargs = kwargs
+
+ def __getstate__(self):
+ return {"_ezpickle_args": self._ezpickle_args, "_ezpickle_kwargs": self._ezpickle_kwargs}
+
+ def __setstate__(self, d):
+ out = type(self)(*d["_ezpickle_args"], **d["_ezpickle_kwargs"])
+ self.__dict__.update(out.__dict__)
+
+
+def set_global_seeds(i):
+ try:
+ import MPI
+ rank = MPI.COMM_WORLD.Get_rank()
+ except ImportError:
+ rank = 0
+
+ myseed = i + 1000 * rank if i is not None else None
+ try:
+ import tensorflow as tf
+ tf.random.set_seed(myseed)
+ except ImportError:
+ pass
+ np.random.seed(myseed)
+ random.seed(myseed)
+
+
+def pretty_eta(seconds_left):
+ """Print the number of seconds in human readable format.
+
+ Examples:
+ 2 days
+ 2 hours and 37 minutes
+ less than a minute
+
+ Paramters
+ ---------
+ seconds_left: int
+ Number of seconds to be converted to the ETA
+ Returns
+ -------
+ eta: str
+ String representing the pretty ETA.
+ """
+ minutes_left = seconds_left // 60
+ seconds_left %= 60
+ hours_left = minutes_left // 60
+ minutes_left %= 60
+ days_left = hours_left // 24
+ hours_left %= 24
+
+ def helper(cnt, name):
+ return "{} {}{}".format(str(cnt), name, ('s' if cnt > 1 else ''))
+
+ if days_left > 0:
+ msg = helper(days_left, 'day')
+ if hours_left > 0:
+ msg += ' and ' + helper(hours_left, 'hour')
+ return msg
+ if hours_left > 0:
+ msg = helper(hours_left, 'hour')
+ if minutes_left > 0:
+ msg += ' and ' + helper(minutes_left, 'minute')
+ return msg
+ if minutes_left > 0:
+ return helper(minutes_left, 'minute')
+ return 'less than a minute'
+
+
+class RunningAvg(object):
+ def __init__(self, gamma, init_value=None):
+ """Keep a running estimate of a quantity. This is a bit like mean
+ but more sensitive to recent changes.
+
+ Parameters
+ ----------
+ gamma: float
+ Must be between 0 and 1, where 0 is the most sensitive to recent
+ changes.
+ init_value: float or None
+ Initial value of the estimate. If None, it will be set on the first update.
+ """
+ self._value = init_value
+ self._gamma = gamma
+
+ def update(self, new_val):
+ """Update the estimate.
+
+ Parameters
+ ----------
+ new_val: float
+ new observated value of estimated quantity.
+ """
+ if self._value is None:
+ self._value = new_val
+ else:
+ self._value = self._gamma * self._value + (1.0 - self._gamma) * new_val
+
+ def __float__(self):
+ """Get the current estimate"""
+ return self._value
+
+def boolean_flag(parser, name, default=False, help=None):
+ """Add a boolean flag to argparse parser.
+
+ Parameters
+ ----------
+ parser: argparse.Parser
+ parser to add the flag to
+ name: str
+ --<name> will enable the flag, while --no-<name> will disable it
+ default: bool or None
+ default value of the flag
+ help: str
+ help string for the flag
+ """
+ dest = name.replace('-', '_')
+ parser.add_argument("--" + name, action="store_true", default=default, dest=dest, help=help)
+ parser.add_argument("--no-" + name, action="store_false", dest=dest)
+
+
+def get_wrapper_by_name(env, classname):
+ """Given an a gym environment possibly wrapped multiple times, returns a wrapper
+ of class named classname or raises ValueError if no such wrapper was applied
+
+ Parameters
+ ----------
+ env: gym.Env of gym.Wrapper
+ gym environment
+ classname: str
+ name of the wrapper
+
+ Returns
+ -------
+ wrapper: gym.Wrapper
+ wrapper named classname
+ """
+ currentenv = env
+ while True:
+ if classname == currentenv.class_name():
+ return currentenv
+ elif isinstance(currentenv, gym.Wrapper):
+ currentenv = currentenv.env
+ else:
+ raise ValueError("Couldn't find wrapper named %s" % classname)
+
+
+def relatively_safe_pickle_dump(obj, path, compression=False):
+ """This is just like regular pickle dump, except from the fact that failure cases are
+ different:
+
+ - It's never possible that we end up with a pickle in corrupted state.
+ - If a there was a different file at the path, that file will remain unchanged in the
+ even of failure (provided that filesystem rename is atomic).
+ - it is sometimes possible that we end up with useless temp file which needs to be
+ deleted manually (it will be removed automatically on the next function call)
+
+ The indended use case is periodic checkpoints of experiment state, such that we never
+ corrupt previous checkpoints if the current one fails.
+
+ Parameters
+ ----------
+ obj: object
+ object to pickle
+ path: str
+ path to the output file
+ compression: bool
+ if true pickle will be compressed
+ """
+ temp_storage = path + ".relatively_safe"
+ if compression:
+ # Using gzip here would be simpler, but the size is limited to 2GB
+ with tempfile.NamedTemporaryFile() as uncompressed_file:
+ pickle.dump(obj, uncompressed_file)
+ uncompressed_file.file.flush()
+ with zipfile.ZipFile(temp_storage, "w", compression=zipfile.ZIP_DEFLATED) as myzip:
+ myzip.write(uncompressed_file.name, "data")
+ else:
+ with open(temp_storage, "wb") as f:
+ pickle.dump(obj, f)
+ os.rename(temp_storage, path)
+
+
+def pickle_load(path, compression=False):
+ """Unpickle a possible compressed pickle.
+
+ Parameters
+ ----------
+ path: str
+ path to the output file
+ compression: bool
+ if true assumes that pickle was compressed when created and attempts decompression.
+
+ Returns
+ -------
+ obj: object
+ the unpickled object
+ """
+
+ if compression:
+ with zipfile.ZipFile(path, "r", compression=zipfile.ZIP_DEFLATED) as myzip:
+ with myzip.open("data") as f:
+ return pickle.load(f)
+ else:
+ with open(path, "rb") as f:
+ return pickle.load(f)
diff --git a/baselines/baselines/common/models.py b/baselines/baselines/common/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..21e1da1fa204f95498f36ea97632d6c9ef5b306b
--- /dev/null
+++ b/baselines/baselines/common/models.py
@@ -0,0 +1,117 @@
+import numpy as np
+import tensorflow as tf
+from baselines.a2c.utils import ortho_init, conv
+
+mapping = {}
+
+def register(name):
+ def _thunk(func):
+ mapping[name] = func
+ return func
+ return _thunk
+
+
+def nature_cnn(input_shape, **conv_kwargs):
+ """
+ CNN from Nature paper.
+ """
+ print('input shape is {}'.format(input_shape))
+ x_input = tf.keras.Input(shape=input_shape, dtype=tf.uint8)
+ h = x_input
+ h = tf.cast(h, tf.float32) / 255.
+ h = conv('c1', nf=32, rf=8, stride=4, activation='relu', init_scale=np.sqrt(2))(h)
+ h2 = conv('c2', nf=64, rf=4, stride=2, activation='relu', init_scale=np.sqrt(2))(h)
+ h3 = conv('c3', nf=64, rf=3, stride=1, activation='relu', init_scale=np.sqrt(2))(h2)
+ h3 = tf.keras.layers.Flatten()(h3)
+ h3 = tf.keras.layers.Dense(units=512, kernel_initializer=ortho_init(np.sqrt(2)),
+ name='fc1', activation='relu')(h3)
+ network = tf.keras.Model(inputs=[x_input], outputs=[h3])
+ return network
+
+@register("mlp")
+def mlp(num_layers=2, num_hidden=64, activation=tf.tanh):
+ """
+ Stack of fully-connected layers to be used in a policy / q-function approximator
+
+ Parameters:
+ ----------
+
+ num_layers: int number of fully-connected layers (default: 2)
+
+ num_hidden: int size of fully-connected layers (default: 64)
+
+ activation: activation function (default: tf.tanh)
+
+ Returns:
+ -------
+
+ function that builds fully connected network with a given input tensor / placeholder
+ """
+ def network_fn(input_shape):
+ print('input shape is {}'.format(input_shape))
+ x_input = tf.keras.Input(shape=input_shape)
+ # h = tf.keras.layers.Flatten(x_input)
+ h = x_input
+ for i in range(num_layers):
+ h = tf.keras.layers.Dense(units=num_hidden, kernel_initializer=ortho_init(np.sqrt(2)),
+ name='mlp_fc{}'.format(i), activation=activation)(h)
+
+ network = tf.keras.Model(inputs=[x_input], outputs=[h])
+ return network
+
+ return network_fn
+
+
+@register("cnn")
+def cnn(**conv_kwargs):
+ def network_fn(input_shape):
+ return nature_cnn(input_shape, **conv_kwargs)
+ return network_fn
+
+
+@register("conv_only")
+def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
+ '''
+ convolutions-only net
+ Parameters:
+ ----------
+ conv: list of triples (filter_number, filter_size, stride) specifying parameters for each layer.
+ Returns:
+ function that takes tensorflow tensor as input and returns the output of the last convolutional layer
+ '''
+
+ def network_fn(input_shape):
+ print('input shape is {}'.format(input_shape))
+ x_input = tf.keras.Input(shape=input_shape, dtype=tf.uint8)
+ h = x_input
+ h = tf.cast(h, tf.float32) / 255.
+ with tf.name_scope("convnet"):
+ for num_outputs, kernel_size, stride in convs:
+ h = tf.keras.layers.Conv2D(
+ filters=num_outputs, kernel_size=kernel_size, strides=stride,
+ activation='relu', **conv_kwargs)(h)
+
+ network = tf.keras.Model(inputs=[x_input], outputs=[h])
+ return network
+ return network_fn
+
+
+def get_network_builder(name):
+ """
+ If you want to register your own network outside models.py, you just need:
+
+ Usage Example:
+ -------------
+ from baselines.common.models import register
+ @register("your_network_name")
+ def your_network_define(**net_kwargs):
+ ...
+ return network_fn
+
+ """
+ if callable(name):
+ return name
+ elif name in mapping:
+ return mapping[name]
+ else:
+ raise ValueError('Unknown network type: {}'.format(name))
diff --git a/baselines/baselines/common/mpi_adam.py b/baselines/baselines/common/mpi_adam.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccda451ac90216643f5104e930cf4199696cb5de
--- /dev/null
+++ b/baselines/baselines/common/mpi_adam.py
@@ -0,0 +1,60 @@
+import baselines.common.tf_util as U
+import numpy as np
+try:
+ from mpi4py import MPI
+except ImportError:
+ MPI = None
+
+
+class MpiAdam(object):
+ def __init__(self, var_list, *, beta1=0.9, beta2=0.999, epsilon=1e-08, scale_grad_by_procs=True, comm=None):
+ self.var_list = var_list
+ self.beta1 = beta1
+ self.beta2 = beta2
+ self.epsilon = epsilon
+ self.scale_grad_by_procs = scale_grad_by_procs
+ size = sum(U.numel(v) for v in var_list)
+ self.m = np.zeros(size, 'float32')
+ self.v = np.zeros(size, 'float32')
+ self.t = 0
+ self.setfromflat = U.SetFromFlat(var_list)
+ self.getflat = U.GetFlat(var_list)
+ self.comm = MPI.COMM_WORLD if comm is None and MPI is not None else comm
+
+ def update(self, localg, stepsize):
+ if self.t % 100 == 0:
+ self.check_synced()
+ localg = localg.astype('float32')
+ if self.comm is not None:
+ globalg = np.zeros_like(localg)
+ self.comm.Allreduce(localg, globalg, op=MPI.SUM)
+ if self.scale_grad_by_procs:
+ globalg /= self.comm.Get_size()
+ else:
+ globalg = np.copy(localg)
+
+ self.t += 1
+ a = stepsize * np.sqrt(1 - self.beta2**self.t)/(1 - self.beta1**self.t)
+ self.m = self.beta1 * self.m + (1 - self.beta1) * globalg
+ self.v = self.beta2 * self.v + (1 - self.beta2) * (globalg * globalg)
+ step = (- a) * self.m / (np.sqrt(self.v) + self.epsilon)
+ self.setfromflat(self.getflat() + step)
+
+ def sync(self):
+ if self.comm is None:
+ return
+ theta = self.getflat()
+ self.comm.Bcast(theta, root=0)
+ self.setfromflat(theta)
+
+ def check_synced(self):
+ if self.comm is None:
+ return
+ if self.comm.Get_rank() == 0: # this is root
+ theta = self.getflat()
+ self.comm.Bcast(theta, root=0)
+ else:
+ thetalocal = self.getflat()
+ thetaroot = np.empty_like(thetalocal)
+ self.comm.Bcast(thetaroot, root=0)
+ assert (thetaroot == thetalocal).all(), (thetaroot, thetalocal)
diff --git a/baselines/baselines/common/mpi_adam_optimizer.py b/baselines/baselines/common/mpi_adam_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..04f3e5da64ffb1c3c0f88a41330086d519ef8325
--- /dev/null
+++ b/baselines/baselines/common/mpi_adam_optimizer.py
@@ -0,0 +1,59 @@
+import numpy as np
+import tensorflow as tf
+try:
+ from mpi4py import MPI
+except ImportError:
+ MPI = None
+
+class MpiAdamOptimizer(tf.Module):
+ """Adam optimizer that averages gradients across mpi processes."""
+ def __init__(self, comm, var_list):
+ self.var_list = var_list
+ self.comm = comm
+ self.beta1 = 0.9
+ self.beta2 = 0.999
+ self.epsilon = 1e-08
+ self.t = tf.Variable(0, name='step', dtype=tf.int32)
+ var_shapes = [v.shape.as_list() for v in var_list]
+ self.var_sizes = [int(np.prod(s)) for s in var_shapes]
+ self.flat_var_size = sum(self.var_sizes)
+ self.m = tf.Variable(np.zeros(self.flat_var_size, 'float32'))
+ self.v = tf.Variable(np.zeros(self.flat_var_size, 'float32'))
+
+ def apply_gradients(self, flat_grad, lr):
+ buf = np.zeros(self.flat_var_size, np.float32)
+ self.comm.Allreduce(flat_grad.numpy(), buf, op=MPI.SUM)
+ avg_flat_grad = np.divide(buf, float(self.comm.Get_size()))
+ self._apply_gradients(tf.constant(avg_flat_grad), lr)
+ if self.t.numpy() % 100 == 0:
+ check_synced(tf.reduce_sum(self.var_list[0]).numpy())
+
+ @tf.function
+ def _apply_gradients(self, avg_flat_grad, lr):
+ self.t.assign_add(1)
+ t = tf.cast(self.t, tf.float32)
+ a = lr * tf.math.sqrt(1 - tf.math.pow(self.beta2, t)) / (1 - tf.math.pow(self.beta1, t))
+ self.m.assign(self.beta1 * self.m + (1 - self.beta1) * avg_flat_grad)
+ self.v.assign(self.beta2 * self.v + (1 - self.beta2) * tf.math.square(avg_flat_grad))
+ flat_step = (- a) * self.m / (tf.math.sqrt(self.v) + self.epsilon)
+ var_steps = tf.split(flat_step, self.var_sizes, axis=0)
+ for var_step, var in zip(var_steps, self.var_list):
+ var.assign_add(tf.reshape(var_step, var.shape))
+
+
+def check_synced(localval, comm=None):
+ """
+ It's common to forget to initialize your variables to the same values, or
+ (less commonly) if you update them in some other way than adam, to get them out of sync.
+ This function checks that variables on all MPI workers are the same, and raises
+ an AssertionError otherwise
+
+ Arguments:
+ comm: MPI communicator
+ localval: list of local variables (list of variables on current worker to be compared with the other workers)
+ """
+ comm = comm or MPI.COMM_WORLD
+ vals = comm.gather(localval)
+ if comm.rank == 0:
+ assert all(val==vals[0] for val in vals[1:]),\
+ 'MpiAdamOptimizer detected that different workers have different weights: {}'.format(vals)
diff --git a/baselines/baselines/common/mpi_fork.py b/baselines/baselines/common/mpi_fork.py
new file mode 100644
index 0000000000000000000000000000000000000000..07b555e4fe7bbf121298058a3a89bb30da73a6bd
--- /dev/null
+++ b/baselines/baselines/common/mpi_fork.py
@@ -0,0 +1,23 @@
+import os, subprocess, sys
+
+def mpi_fork(n, bind_to_core=False):
+ """Re-launches the current script with workers
+ Returns "parent" for original parent, "child" for MPI children
+ """
+ if n<=1:
+ return "child"
+ if os.getenv("IN_MPI") is None:
+ env = os.environ.copy()
+ env.update(
+ MKL_NUM_THREADS="1",
+ OMP_NUM_THREADS="1",
+ IN_MPI="1"
+ )
+ args = ["mpirun", "-np", str(n)]
+ if bind_to_core:
+ args += ["-bind-to", "core"]
+ args += [sys.executable] + sys.argv
+ subprocess.check_call(args, env=env)
+ return "parent"
+ else:
+ return "child"
diff --git a/baselines/baselines/common/mpi_moments.py b/baselines/baselines/common/mpi_moments.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a97a4300635d0f95a93070d0f489e7b2b457d37
--- /dev/null
+++ b/baselines/baselines/common/mpi_moments.py
@@ -0,0 +1,60 @@
+from mpi4py import MPI
+import numpy as np
+from baselines.common import zipsame
+
+
+def mpi_mean(x, axis=0, comm=None, keepdims=False):
+ x = np.asarray(x)
+ assert x.ndim > 0
+ if comm is None: comm = MPI.COMM_WORLD
+ xsum = x.sum(axis=axis, keepdims=keepdims)
+ n = xsum.size
+ localsum = np.zeros(n+1, x.dtype)
+ localsum[:n] = xsum.ravel()
+ localsum[n] = x.shape[axis]
+ globalsum = np.zeros_like(localsum)
+ comm.Allreduce(localsum, globalsum, op=MPI.SUM)
+ return globalsum[:n].reshape(xsum.shape) / globalsum[n], globalsum[n]
+
+def mpi_moments(x, axis=0, comm=None, keepdims=False):
+ x = np.asarray(x)
+ assert x.ndim > 0
+ mean, count = mpi_mean(x, axis=axis, comm=comm, keepdims=True)
+ sqdiffs = np.square(x - mean)
+ meansqdiff, count1 = mpi_mean(sqdiffs, axis=axis, comm=comm, keepdims=True)
+ assert count1 == count
+ std = np.sqrt(meansqdiff)
+ if not keepdims:
+ newshape = mean.shape[:axis] + mean.shape[axis+1:]
+ mean = mean.reshape(newshape)
+ std = std.reshape(newshape)
+ return mean, std, count
+
+
+def test_runningmeanstd():
+ import subprocess
+ subprocess.check_call(['mpirun', '-np', '3',
+ 'python','-c',
+ 'from baselines.common.mpi_moments import _helper_runningmeanstd; _helper_runningmeanstd()'])
+
+def _helper_runningmeanstd():
+ comm = MPI.COMM_WORLD
+ np.random.seed(0)
+ for (triple,axis) in [
+ ((np.random.randn(3), np.random.randn(4), np.random.randn(5)),0),
+ ((np.random.randn(3,2), np.random.randn(4,2), np.random.randn(5,2)),0),
+ ((np.random.randn(2,3), np.random.randn(2,4), np.random.randn(2,4)),1),
+ ]:
+
+
+ x = np.concatenate(triple, axis=axis)
+ ms1 = [x.mean(axis=axis), x.std(axis=axis), x.shape[axis]]
+
+
+ ms2 = mpi_moments(triple[comm.Get_rank()],axis=axis)
+
+ for (a1,a2) in zipsame(ms1, ms2):
+ print(a1, a2)
+ assert np.allclose(a1, a2)
+ print("ok!")
+
diff --git a/baselines/baselines/common/mpi_running_mean_std.py b/baselines/baselines/common/mpi_running_mean_std.py
new file mode 100644
index 0000000000000000000000000000000000000000..51ee359fc1a8385431a33cbac516810a57eebeb9
--- /dev/null
+++ b/baselines/baselines/common/mpi_running_mean_std.py
@@ -0,0 +1,56 @@
+try:
+ from mpi4py import MPI
+except ImportError:
+ MPI = None
+
+import tensorflow as tf, numpy as np
+
+class RunningMeanStd(tf.Module):
+ # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
+ def __init__(self, epsilon=1e-2, shape=(), default_clip_range=np.inf):
+
+ self._sum = tf.Variable(
+ initial_value=np.zeros(shape=shape, dtype=np.float64),
+ dtype=tf.float64,
+ name="runningsum", trainable=False)
+ self._sumsq = tf.Variable(
+ initial_value=np.full(shape=shape, fill_value=epsilon, dtype=np.float64),
+ dtype=tf.float64,
+ name="runningsumsq", trainable=False)
+ self._count = tf.Variable(
+ initial_value=epsilon,
+ dtype=tf.float64,
+ name="count", trainable=False)
+ self.shape = shape
+ self.epsilon = epsilon
+ self.default_clip_range = default_clip_range
+
+ def update(self, x):
+ x = x.astype('float64')
+ n = int(np.prod(self.shape))
+ addvec = np.concatenate([x.sum(axis=0).ravel(), np.square(x).sum(axis=0).ravel(), np.array([len(x)],dtype='float64')])
+ totalvec = np.zeros(n*2+1, 'float64')
+ if MPI is not None:
+ # totalvec = np.zeros(n*2+1, 'float64')
+ MPI.COMM_WORLD.Allreduce(addvec, totalvec, op=MPI.SUM)
+ # else:
+ # totalvec = addvec
+ self._sum.assign_add(totalvec[0:n].reshape(self.shape))
+ self._sumsq.assign_add(totalvec[n:2*n].reshape(self.shape))
+ self._count.assign_add(totalvec[2*n])
+
+ @property
+ def mean(self):
+ return tf.cast(self._sum / self._count, tf.float32)
+
+ @property
+ def std(self):
+ return tf.sqrt(tf.maximum(tf.cast(self._sumsq / self._count, tf.float32) - tf.square(self.mean), self.epsilon))
+
+ def normalize(self, v, clip_range=None):
+ if clip_range is None:
+ clip_range = self.default_clip_range
+ return tf.clip_by_value((v - self.mean) / self.std, -clip_range, clip_range)
+
+ def denormalize(self, v):
+ return self.mean + v * self.std
\ No newline at end of file
diff --git a/baselines/baselines/common/mpi_util.py b/baselines/baselines/common/mpi_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..05df247b04cfb5421dbfe076fdca58e2d3485331
--- /dev/null
+++ b/baselines/baselines/common/mpi_util.py
@@ -0,0 +1,131 @@
+from collections import defaultdict
+import os, numpy as np
+import platform
+import shutil
+import subprocess
+import warnings
+import sys
+
+try:
+ from mpi4py import MPI
+except ImportError:
+ MPI = None
+
+
+def sync_from_root(variables, comm=None):
+ """
+ Send the root node's parameters to every worker.
+ Arguments:
+ variables: all parameter variables including optimizer's
+ """
+ if comm is None: comm = MPI.COMM_WORLD
+ values = comm.bcast([var.numpy() for var in variables])
+ for (var, val) in zip(variables, values):
+ var.assign(val)
+
+def gpu_count():
+ """
+ Count the GPUs on this machine.
+ """
+ if shutil.which('nvidia-smi') is None:
+ return 0
+ output = subprocess.check_output(['nvidia-smi', '--query-gpu=gpu_name', '--format=csv'])
+ return max(0, len(output.split(b'\n')) - 2)
+
+def setup_mpi_gpus():
+ """
+ Set CUDA_VISIBLE_DEVICES to MPI rank if not already set
+ """
+ if 'CUDA_VISIBLE_DEVICES' not in os.environ:
+ if sys.platform == 'darwin': # This Assumes if you're on OSX you're just
+ ids = [] # doing a smoke test and don't want GPUs
+ else:
+ lrank, _lsize = get_local_rank_size(MPI.COMM_WORLD)
+ ids = [lrank]
+ os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, ids))
+
+def get_local_rank_size(comm):
+ """
+ Returns the rank of each process on its machine
+ The processes on a given machine will be assigned ranks
+ 0, 1, 2, ..., N-1,
+ where N is the number of processes on this machine.
+
+ Useful if you want to assign one gpu per machine
+ """
+ this_node = platform.node()
+ ranks_nodes = comm.allgather((comm.Get_rank(), this_node))
+ node2rankssofar = defaultdict(int)
+ local_rank = None
+ for (rank, node) in ranks_nodes:
+ if rank == comm.Get_rank():
+ local_rank = node2rankssofar[node]
+ node2rankssofar[node] += 1
+ assert local_rank is not None
+ return local_rank, node2rankssofar[this_node]
+
+def share_file(comm, path):
+ """
+ Copies the file from rank 0 to all other ranks
+ Puts it in the same place on all machines
+ """
+ localrank, _ = get_local_rank_size(comm)
+ if comm.Get_rank() == 0:
+ with open(path, 'rb') as fh:
+ data = fh.read()
+ comm.bcast(data)
+ else:
+ data = comm.bcast(None)
+ if localrank == 0:
+ os.makedirs(os.path.dirname(path), exist_ok=True)
+ with open(path, 'wb') as fh:
+ fh.write(data)
+ comm.Barrier()
+
+def dict_gather(comm, d, op='mean', assert_all_have_data=True):
+ """
+ Perform a reduction operation over dicts
+ """
+ if comm is None: return d
+ alldicts = comm.allgather(d)
+ size = comm.size
+ k2li = defaultdict(list)
+ for d in alldicts:
+ for (k,v) in d.items():
+ k2li[k].append(v)
+ result = {}
+ for (k,li) in k2li.items():
+ if assert_all_have_data:
+ assert len(li)==size, "only %i out of %i MPI workers have sent '%s'" % (len(li), size, k)
+ if op=='mean':
+ result[k] = np.mean(li, axis=0)
+ elif op=='sum':
+ result[k] = np.sum(li, axis=0)
+ else:
+ assert 0, op
+ return result
+
+def mpi_weighted_mean(comm, local_name2valcount):
+ """
+ Perform a weighted average over dicts that are each on a different node
+ Input: local_name2valcount: dict mapping key -> (value, count)
+ Returns: key -> mean
+ """
+ all_name2valcount = comm.gather(local_name2valcount)
+ if comm.rank == 0:
+ name2sum = defaultdict(float)
+ name2count = defaultdict(float)
+ for n2vc in all_name2valcount:
+ for (name, (val, count)) in n2vc.items():
+ try:
+ val = float(val)
+ except ValueError:
+ if comm.rank == 0:
+ warnings.warn('WARNING: tried to compute mean on non-float {}={}'.format(name, val))
+ else:
+ name2sum[name] += val * count
+ name2count[name] += count
+ return {name : name2sum[name] / name2count[name] for name in name2sum}
+ else:
+ return {}
+
diff --git a/baselines/baselines/common/plot_util.py b/baselines/baselines/common/plot_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..e15c5082a4d3bd117d037771b93ab466af2aca0d
--- /dev/null
+++ b/baselines/baselines/common/plot_util.py
@@ -0,0 +1,434 @@
+import matplotlib.pyplot as plt
+import os.path as osp
+import json
+import os
+import numpy as np
+import pandas
+from collections import defaultdict, namedtuple
+from baselines.bench import monitor
+from baselines.logger import read_json, read_csv
+
+def smooth(y, radius, mode='two_sided', valid_only=False):
+ '''
+ Smooth signal y, where radius is determines the size of the window
+
+ mode='twosided':
+ average over the window [max(index - radius, 0), min(index + radius, len(y)-1)]
+ mode='causal':
+ average over the window [max(index - radius, 0), index]
+
+ valid_only: put nan in entries where the full-sized window is not available
+
+ '''
+ assert mode in ('two_sided', 'causal')
+ if len(y) < 2*radius+1:
+ return np.ones_like(y) * y.mean()
+ elif mode == 'two_sided':
+ convkernel = np.ones(2 * radius+1)
+ out = np.convolve(y, convkernel,mode='same') / np.convolve(np.ones_like(y), convkernel, mode='same')
+ if valid_only:
+ out[:radius] = out[-radius:] = np.nan
+ elif mode == 'causal':
+ convkernel = np.ones(radius)
+ out = np.convolve(y, convkernel,mode='full') / np.convolve(np.ones_like(y), convkernel, mode='full')
+ out = out[:-radius+1]
+ if valid_only:
+ out[:radius] = np.nan
+ return out
+
+def one_sided_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
+ '''
+ perform one-sided (causal) EMA (exponential moving average)
+ smoothing and resampling to an even grid with n points.
+ Does not do extrapolation, so we assume
+ xolds[0] <= low && high <= xolds[-1]
+
+ Arguments:
+
+ xolds: array or list - x values of data. Needs to be sorted in ascending order
+ yolds: array of list - y values of data. Has to have the same length as xolds
+
+ low: float - min value of the new x grid. By default equals to xolds[0]
+ high: float - max value of the new x grid. By default equals to xolds[-1]
+
+ n: int - number of points in new x grid
+
+ decay_steps: float - EMA decay factor, expressed in new x grid steps.
+
+ low_counts_threshold: float or int
+ - y values with counts less than this value will be set to NaN
+
+ Returns:
+ tuple sum_ys, count_ys where
+ xs - array with new x grid
+ ys - array of EMA of y at each point of the new x grid
+ count_ys - array of EMA of y counts at each point of the new x grid
+
+ '''
+
+ low = xolds[0] if low is None else low
+ high = xolds[-1] if high is None else high
+
+ assert xolds[0] <= low, 'low = {} < xolds[0] = {} - extrapolation not permitted!'.format(low, xolds[0])
+ assert xolds[-1] >= high, 'high = {} > xolds[-1] = {} - extrapolation not permitted!'.format(high, xolds[-1])
+ assert len(xolds) == len(yolds), 'length of xolds ({}) and yolds ({}) do not match!'.format(len(xolds), len(yolds))
+
+
+ xolds = xolds.astype('float64')
+ yolds = yolds.astype('float64')
+
+ luoi = 0 # last unused old index
+ sum_y = 0.
+ count_y = 0.
+ xnews = np.linspace(low, high, n)
+ decay_period = (high - low) / (n - 1) * decay_steps
+ interstep_decay = np.exp(- 1. / decay_steps)
+ sum_ys = np.zeros_like(xnews)
+ count_ys = np.zeros_like(xnews)
+ for i in range(n):
+ xnew = xnews[i]
+ sum_y *= interstep_decay
+ count_y *= interstep_decay
+ while True:
+ if luoi >= len(xolds):
+ break
+ xold = xolds[luoi]
+ if xold <= xnew:
+ decay = np.exp(- (xnew - xold) / decay_period)
+ sum_y += decay * yolds[luoi]
+ count_y += decay
+ luoi += 1
+ else:
+ break
+ sum_ys[i] = sum_y
+ count_ys[i] = count_y
+
+ ys = sum_ys / count_ys
+ ys[count_ys < low_counts_threshold] = np.nan
+
+ return xnews, ys, count_ys
+
+def symmetric_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
+ '''
+ perform symmetric EMA (exponential moving average)
+ smoothing and resampling to an even grid with n points.
+ Does not do extrapolation, so we assume
+ xolds[0] <= low && high <= xolds[-1]
+
+ Arguments:
+
+ xolds: array or list - x values of data. Needs to be sorted in ascending order
+ yolds: array of list - y values of data. Has to have the same length as xolds
+
+ low: float - min value of the new x grid. By default equals to xolds[0]
+ high: float - max value of the new x grid. By default equals to xolds[-1]
+
+ n: int - number of points in new x grid
+
+ decay_steps: float - EMA decay factor, expressed in new x grid steps.
+
+ low_counts_threshold: float or int
+ - y values with counts less than this value will be set to NaN
+
+ Returns:
+ tuple sum_ys, count_ys where
+ xs - array with new x grid
+ ys - array of EMA of y at each point of the new x grid
+ count_ys - array of EMA of y counts at each point of the new x grid
+
+ '''
+ xs, ys1, count_ys1 = one_sided_ema(xolds, yolds, low, high, n, decay_steps, low_counts_threshold=0)
+ _, ys2, count_ys2 = one_sided_ema(-xolds[::-1], yolds[::-1], -high, -low, n, decay_steps, low_counts_threshold=0)
+ ys2 = ys2[::-1]
+ count_ys2 = count_ys2[::-1]
+ count_ys = count_ys1 + count_ys2
+ ys = (ys1 * count_ys1 + ys2 * count_ys2) / count_ys
+ ys[count_ys < low_counts_threshold] = np.nan
+ return xs, ys, count_ys
+
+Result = namedtuple('Result', 'monitor progress dirname metadata')
+Result.__new__.__defaults__ = (None,) * len(Result._fields)
+
+def load_results(root_dir_or_dirs, enable_progress=True, enable_monitor=True, verbose=False):
+ '''
+ load summaries of runs from a list of directories (including subdirectories)
+ Arguments:
+
+ enable_progress: bool - if True, will attempt to load data from progress.csv files (data saved by logger). Default: True
+
+ enable_monitor: bool - if True, will attempt to load data from monitor.csv files (data saved by Monitor environment wrapper). Default: True
+
+ verbose: bool - if True, will print out list of directories from which the data is loaded. Default: False
+
+
+ Returns:
+ List of Result objects with the following fields:
+ - dirname - path to the directory data was loaded from
+ - metadata - run metadata (such as command-line arguments and anything else in metadata.json file
+ - monitor - if enable_monitor is True, this field contains pandas dataframe with loaded monitor.csv file (or aggregate of all *.monitor.csv files in the directory)
+ - progress - if enable_progress is True, this field contains pandas dataframe with loaded progress.csv file
+ '''
+ import re
+ if isinstance(root_dir_or_dirs, str):
+ rootdirs = [osp.expanduser(root_dir_or_dirs)]
+ else:
+ rootdirs = [osp.expanduser(d) for d in root_dir_or_dirs]
+ allresults = []
+ for rootdir in rootdirs:
+ assert osp.exists(rootdir), "%s doesn't exist"%rootdir
+ for dirname, dirs, files in os.walk(rootdir):
+ if '-proc' in dirname:
+ files[:] = []
+ continue
+ monitor_re = re.compile(r'(\d+\.)?(\d+\.)?monitor\.csv')
+ if set(['metadata.json', 'monitor.json', 'progress.json', 'progress.csv']).intersection(files) or \
+ any([f for f in files if monitor_re.match(f)]): # also match monitor files like 0.1.monitor.csv
+ # used to be uncommented, which means do not go deeper than current directory if any of the data files
+ # are found
+ # dirs[:] = []
+ result = {'dirname' : dirname}
+ if "metadata.json" in files:
+ with open(osp.join(dirname, "metadata.json"), "r") as fh:
+ result['metadata'] = json.load(fh)
+ progjson = osp.join(dirname, "progress.json")
+ progcsv = osp.join(dirname, "progress.csv")
+ if enable_progress:
+ if osp.exists(progjson):
+ result['progress'] = pandas.DataFrame(read_json(progjson))
+ elif osp.exists(progcsv):
+ try:
+ result['progress'] = read_csv(progcsv)
+ except pandas.errors.EmptyDataError:
+ print('skipping progress file in ', dirname, 'empty data')
+ else:
+ if verbose: print('skipping %s: no progress file'%dirname)
+
+ if enable_monitor:
+ try:
+ result['monitor'] = pandas.DataFrame(monitor.load_results(dirname))
+ except monitor.LoadMonitorResultsError:
+ print('skipping %s: no monitor files'%dirname)
+ except Exception as e:
+ print('exception loading monitor file in %s: %s'%(dirname, e))
+
+ if result.get('monitor') is not None or result.get('progress') is not None:
+ allresults.append(Result(**result))
+ if verbose:
+ print('successfully loaded %s'%dirname)
+
+ if verbose: print('loaded %i results'%len(allresults))
+ return allresults
+
+COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'purple', 'pink',
+ 'brown', 'orange', 'teal', 'lightblue', 'lime', 'lavender', 'turquoise',
+ 'darkgreen', 'tan', 'salmon', 'gold', 'darkred', 'darkblue']
+
+
+def default_xy_fn(r):
+ x = np.cumsum(r.monitor.l)
+ y = smooth(r.monitor.r, radius=10)
+ return x,y
+
+def default_split_fn(r):
+ import re
+ # match name between slash and -<digits> at the end of the string
+ # (slash in the beginning or -<digits> in the end or either may be missing)
+ match = re.search(r'[^/-]+(?=(-\d+)?\Z)', r.dirname)
+ if match:
+ return match.group(0)
+
+def plot_results(
+ allresults, *,
+ xy_fn=default_xy_fn,
+ split_fn=default_split_fn,
+ group_fn=default_split_fn,
+ average_group=False,
+ shaded_std=True,
+ shaded_err=True,
+ figsize=None,
+ legend_outside=False,
+ resample=0,
+ smooth_step=1.0,
+ tiling='vertical',
+ xlabel=None,
+ ylabel=None
+):
+ '''
+ Plot multiple Results objects
+
+ xy_fn: function Result -> x,y - function that converts results objects into tuple of x and y values.
+ By default, x is cumsum of episode lengths, and y is episode rewards
+
+ split_fn: function Result -> hashable - function that converts results objects into keys to split curves into sub-panels by.
+ That is, the results r for which split_fn(r) is different will be put on different sub-panels.
+ By default, the portion of r.dirname between last / and -<digits> is returned. The sub-panels are
+ stacked vertically in the figure.
+
+ group_fn: function Result -> hashable - function that converts results objects into keys to group curves by.
+ That is, the results r for which group_fn(r) is the same will be put into the same group.
+ Curves in the same group have the same color (if average_group is False), or averaged over
+ (if average_group is True). The default value is the same as default value for split_fn
+
+ average_group: bool - if True, will average the curves in the same group and plot the mean. Enables resampling
+ (if resample = 0, will use 512 steps)
+
+ shaded_std: bool - if True (default), the shaded region corresponding to standard deviation of the group of curves will be
+ shown (only applicable if average_group = True)
+
+ shaded_err: bool - if True (default), the shaded region corresponding to error in mean estimate of the group of curves
+ (that is, standard deviation divided by square root of number of curves) will be
+ shown (only applicable if average_group = True)
+
+ figsize: tuple or None - size of the resulting figure (including sub-panels). By default, width is 6 and height is 6 times number of
+ sub-panels.
+
+
+ legend_outside: bool - if True, will place the legend outside of the sub-panels.
+
+ resample: int - if not zero, size of the uniform grid in x direction to resample onto. Resampling is performed via symmetric
+ EMA smoothing (see the docstring for symmetric_ema).
+ Default is zero (no resampling). Note that if average_group is True, resampling is necessary; in that case, default
+ value is 512.
+
+ smooth_step: float - when resampling (i.e. when resample > 0 or average_group is True), use this EMA decay parameter (in units of the new grid step).
+ See docstrings for decay_steps in symmetric_ema or one_sided_ema functions.
+
+ '''
+
+ if split_fn is None: split_fn = lambda _ : ''
+ if group_fn is None: group_fn = lambda _ : ''
+ sk2r = defaultdict(list) # splitkey2results
+ for result in allresults:
+ splitkey = split_fn(result)
+ sk2r[splitkey].append(result)
+ assert len(sk2r) > 0
+ assert isinstance(resample, int), "0: don't resample. <integer>: that many samples"
+ if tiling == 'vertical' or tiling is None:
+ nrows = len(sk2r)
+ ncols = 1
+ elif tiling == 'horizontal':
+ ncols = len(sk2r)
+ nrows = 1
+ elif tiling == 'symmetric':
+ import math
+ N = len(sk2r)
+ largest_divisor = 1
+ for i in range(1, int(math.sqrt(N))+1):
+ if N % i == 0:
+ largest_divisor = i
+ ncols = largest_divisor
+ nrows = N // ncols
+ figsize = figsize or (6 * ncols, 6 * nrows)
+
+ f, axarr = plt.subplots(nrows, ncols, sharex=False, squeeze=False, figsize=figsize)
+
+ groups = list(set(group_fn(result) for result in allresults))
+
+ default_samples = 512
+ if average_group:
+ resample = resample or default_samples
+
+ for (isplit, sk) in enumerate(sorted(sk2r.keys())):
+ g2l = {}
+ g2c = defaultdict(int)
+ sresults = sk2r[sk]
+ gresults = defaultdict(list)
+ idx_row = isplit // ncols
+ idx_col = isplit % ncols
+ ax = axarr[idx_row][idx_col]
+ for result in sresults:
+ group = group_fn(result)
+ g2c[group] += 1
+ x, y = xy_fn(result)
+ if x is None: x = np.arange(len(y))
+ x, y = map(np.asarray, (x, y))
+ if average_group:
+ gresults[group].append((x,y))
+ else:
+ if resample:
+ x, y, counts = symmetric_ema(x, y, x[0], x[-1], resample, decay_steps=smooth_step)
+ l, = ax.plot(x, y, color=COLORS[groups.index(group) % len(COLORS)])
+ g2l[group] = l
+ if average_group:
+ for group in sorted(groups):
+ xys = gresults[group]
+ if not any(xys):
+ continue
+ color = COLORS[groups.index(group) % len(COLORS)]
+ origxs = [xy[0] for xy in xys]
+ minxlen = min(map(len, origxs))
+ def allequal(qs):
+ return all((q==qs[0]).all() for q in qs[1:])
+ if resample:
+ low = max(x[0] for x in origxs)
+ high = min(x[-1] for x in origxs)
+ usex = np.linspace(low, high, resample)
+ ys = []
+ for (x, y) in xys:
+ ys.append(symmetric_ema(x, y, low, high, resample, decay_steps=smooth_step)[1])
+ else:
+ assert allequal([x[:minxlen] for x in origxs]),\
+ 'If you want to average unevenly sampled data, set resample=<number of samples you want>'
+ usex = origxs[0]
+ ys = [xy[1][:minxlen] for xy in xys]
+ ymean = np.mean(ys, axis=0)
+ ystd = np.std(ys, axis=0)
+ ystderr = ystd / np.sqrt(len(ys))
+ l, = axarr[idx_row][idx_col].plot(usex, ymean, color=color)
+ g2l[group] = l
+ if shaded_err:
+ ax.fill_between(usex, ymean - ystderr, ymean + ystderr, color=color, alpha=.4)
+ if shaded_std:
+ ax.fill_between(usex, ymean - ystd, ymean + ystd, color=color, alpha=.2)
+
+
+ # https://matplotlib.org/users/legend_guide.html
+ plt.tight_layout()
+ if any(g2l.keys()):
+ ax.legend(
+ g2l.values(),
+ ['%s (%i)'%(g, g2c[g]) for g in g2l] if average_group else g2l.keys(),
+ loc=2 if legend_outside else None,
+ bbox_to_anchor=(1,1) if legend_outside else None)
+ ax.set_title(sk)
+ # add xlabels, but only to the bottom row
+ if xlabel is not None:
+ for ax in axarr[-1]:
+ plt.sca(ax)
+ plt.xlabel(xlabel)
+ # add ylabels, but only to left column
+ if ylabel is not None:
+ for ax in axarr[:,0]:
+ plt.sca(ax)
+ plt.ylabel(ylabel)
+
+ return f, axarr
+
+def regression_analysis(df):
+ xcols = list(df.columns.copy())
+ xcols.remove('score')
+ ycols = ['score']
+ import statsmodels.api as sm
+ mod = sm.OLS(df[ycols], sm.add_constant(df[xcols]), hasconst=False)
+ res = mod.fit()
+ print(res.summary())
+
+def test_smooth():
+ norig = 100
+ nup = 300
+ ndown = 30
+ xs = np.cumsum(np.random.rand(norig) * 10 / norig)
+ yclean = np.sin(xs)
+ ys = yclean + .1 * np.random.randn(yclean.size)
+ xup, yup, _ = symmetric_ema(xs, ys, xs.min(), xs.max(), nup, decay_steps=nup/ndown)
+ xdown, ydown, _ = symmetric_ema(xs, ys, xs.min(), xs.max(), ndown, decay_steps=ndown/ndown)
+ xsame, ysame, _ = symmetric_ema(xs, ys, xs.min(), xs.max(), norig, decay_steps=norig/ndown)
+ plt.plot(xs, ys, label='orig', marker='x')
+ plt.plot(xup, yup, label='up', marker='x')
+ plt.plot(xdown, ydown, label='down', marker='x')
+ plt.plot(xsame, ysame, label='same', marker='x')
+ plt.plot(xs, yclean, label='clean', marker='x')
+ plt.legend()
+ plt.show()
+
+
diff --git a/baselines/baselines/common/policies.py b/baselines/baselines/common/policies.py
new file mode 100644
index 0000000000000000000000000000000000000000..d566824a484500501a5492c242cd06e5249b2ccc
--- /dev/null
+++ b/baselines/baselines/common/policies.py
@@ -0,0 +1,81 @@
+import tensorflow as tf
+from baselines.a2c.utils import fc
+from baselines.common.distributions import make_pdtype
+
+import gym
+
+
+class PolicyWithValue(tf.Module):
+ """
+ Encapsulates fields and methods for RL policy and value function estimation with shared parameters
+ """
+
+ def __init__(self, ac_space, policy_network, value_network=None, estimate_q=False):
+ """
+ Parameters:
+ ----------
+ ac_space action space
+
+ policy_network keras network for policy
+
+ value_network keras network for value
+
+ estimate_q q value or v value
+
+ """
+
+ self.policy_network = policy_network
+ self.value_network = value_network or policy_network
+ self.estimate_q = estimate_q
+ self.initial_state = None
+
+ # Based on the action space, will select what probability distribution type
+ self.pdtype = make_pdtype(policy_network.output_shape, ac_space, init_scale=0.01)
+
+ if estimate_q:
+ assert isinstance(ac_space, gym.spaces.Discrete)
+ self.value_fc = fc(self.value_network.output_shape, 'q', ac_space.n)
+ else:
+ self.value_fc = fc(self.value_network.output_shape, 'vf', 1)
+
+ @tf.function
+ def step(self, observation):
+ """
+ Compute next action(s) given the observation(s)
+
+ Parameters:
+ ----------
+
+ observation batched observation data
+
+ Returns:
+ -------
+ (action, value estimate, next state, negative log likelihood of the action under current policy parameters) tuple
+ """
+
+ latent = self.policy_network(observation)
+ pd, pi = self.pdtype.pdfromlatent(latent)
+ action = pd.sample()
+ neglogp = pd.neglogp(action)
+ value_latent = self.value_network(observation)
+ vf = tf.squeeze(self.value_fc(value_latent), axis=1)
+ return action, vf, None, neglogp
+
+ @tf.function
+ def value(self, observation):
+ """
+ Compute value estimate(s) given the observation(s)
+
+ Parameters:
+ ----------
+
+ observation observation data (either single or a batch)
+
+ Returns:
+ -------
+ value estimate
+ """
+ value_latent = self.value_network(observation)
+ result = tf.squeeze(self.value_fc(value_latent), axis=1)
+ return result
+
diff --git a/baselines/baselines/common/retro_wrappers.py b/baselines/baselines/common/retro_wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..badbbdd634aa6aedcbba990f32654ea961394c46
--- /dev/null
+++ b/baselines/baselines/common/retro_wrappers.py
@@ -0,0 +1,280 @@
+from collections import deque
+import cv2
+cv2.ocl.setUseOpenCL(False)
+from .atari_wrappers import WarpFrame, ClipRewardEnv, FrameStack, ScaledFloatFrame
+from .wrappers import TimeLimit
+import numpy as np
+import gym
+
+
+class StochasticFrameSkip(gym.Wrapper):
+ def __init__(self, env, n, stickprob):
+ gym.Wrapper.__init__(self, env)
+ self.n = n
+ self.stickprob = stickprob
+ self.curac = None
+ self.rng = np.random.RandomState()
+ self.supports_want_render = hasattr(env, "supports_want_render")
+
+ def reset(self, **kwargs):
+ self.curac = None
+ return self.env.reset(**kwargs)
+
+ def step(self, ac):
+ done = False
+ totrew = 0
+ for i in range(self.n):
+ # First step after reset, use action
+ if self.curac is None:
+ self.curac = ac
+ # First substep, delay with probability=stickprob
+ elif i==0:
+ if self.rng.rand() > self.stickprob:
+ self.curac = ac
+ # Second substep, new action definitely kicks in
+ elif i==1:
+ self.curac = ac
+ if self.supports_want_render and i<self.n-1:
+ ob, rew, done, info = self.env.step(self.curac, want_render=False)
+ else:
+ ob, rew, done, info = self.env.step(self.curac)
+ totrew += rew
+ if done: break
+ return ob, totrew, done, info
+
+ def seed(self, s):
+ self.rng.seed(s)
+
+class PartialFrameStack(gym.Wrapper):
+ def __init__(self, env, k, channel=1):
+ """
+ Stack one channel (channel keyword) from previous frames
+ """
+ gym.Wrapper.__init__(self, env)
+ shp = env.observation_space.shape
+ self.channel = channel
+ self.observation_space = gym.spaces.Box(low=0, high=255,
+ shape=(shp[0], shp[1], shp[2] + k - 1),
+ dtype=env.observation_space.dtype)
+ self.k = k
+ self.frames = deque([], maxlen=k)
+ shp = env.observation_space.shape
+
+ def reset(self):
+ ob = self.env.reset()
+ assert ob.shape[2] > self.channel
+ for _ in range(self.k):
+ self.frames.append(ob)
+ return self._get_ob()
+
+ def step(self, ac):
+ ob, reward, done, info = self.env.step(ac)
+ self.frames.append(ob)
+ return self._get_ob(), reward, done, info
+
+ def _get_ob(self):
+ assert len(self.frames) == self.k
+ return np.concatenate([frame if i==self.k-1 else frame[:,:,self.channel:self.channel+1]
+ for (i, frame) in enumerate(self.frames)], axis=2)
+
+class Downsample(gym.ObservationWrapper):
+ def __init__(self, env, ratio):
+ """
+ Downsample images by a factor of ratio
+ """
+ gym.ObservationWrapper.__init__(self, env)
+ (oldh, oldw, oldc) = env.observation_space.shape
+ newshape = (oldh//ratio, oldw//ratio, oldc)
+ self.observation_space = gym.spaces.Box(low=0, high=255,
+ shape=newshape, dtype=np.uint8)
+
+ def observation(self, frame):
+ height, width, _ = self.observation_space.shape
+ frame = cv2.resize(frame, (width, height), interpolation=cv2.INTER_AREA)
+ if frame.ndim == 2:
+ frame = frame[:,:,None]
+ return frame
+
+class Rgb2gray(gym.ObservationWrapper):
+ def __init__(self, env):
+ """
+ Downsample images by a factor of ratio
+ """
+ gym.ObservationWrapper.__init__(self, env)
+ (oldh, oldw, _oldc) = env.observation_space.shape
+ self.observation_space = gym.spaces.Box(low=0, high=255,
+ shape=(oldh, oldw, 1), dtype=np.uint8)
+
+ def observation(self, frame):
+ frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+ return frame[:,:,None]
+
+
+class MovieRecord(gym.Wrapper):
+ def __init__(self, env, savedir, k):
+ gym.Wrapper.__init__(self, env)
+ self.savedir = savedir
+ self.k = k
+ self.epcount = 0
+ def reset(self):
+ if self.epcount % self.k == 0:
+ self.env.unwrapped.movie_path = self.savedir
+ else:
+ self.env.unwrapped.movie_path = None
+ self.env.unwrapped.movie = None
+ self.epcount += 1
+ return self.env.reset()
+
+class AppendTimeout(gym.Wrapper):
+ def __init__(self, env):
+ gym.Wrapper.__init__(self, env)
+ self.action_space = env.action_space
+ self.timeout_space = gym.spaces.Box(low=np.array([0.0]), high=np.array([1.0]), dtype=np.float32)
+ self.original_os = env.observation_space
+ if isinstance(self.original_os, gym.spaces.Dict):
+ import copy
+ ordered_dict = copy.deepcopy(self.original_os.spaces)
+ ordered_dict['value_estimation_timeout'] = self.timeout_space
+ self.observation_space = gym.spaces.Dict(ordered_dict)
+ self.dict_mode = True
+ else:
+ self.observation_space = gym.spaces.Dict({
+ 'original': self.original_os,
+ 'value_estimation_timeout': self.timeout_space
+ })
+ self.dict_mode = False
+ self.ac_count = None
+ while 1:
+ if not hasattr(env, "_max_episode_steps"): # Looking for TimeLimit wrapper that has this field
+ env = env.env
+ continue
+ break
+ self.timeout = env._max_episode_steps
+
+ def step(self, ac):
+ self.ac_count += 1
+ ob, rew, done, info = self.env.step(ac)
+ return self._process(ob), rew, done, info
+
+ def reset(self):
+ self.ac_count = 0
+ return self._process(self.env.reset())
+
+ def _process(self, ob):
+ fracmissing = 1 - self.ac_count / self.timeout
+ if self.dict_mode:
+ ob['value_estimation_timeout'] = fracmissing
+ else:
+ return { 'original': ob, 'value_estimation_timeout': fracmissing }
+
+class StartDoingRandomActionsWrapper(gym.Wrapper):
+ """
+ Warning: can eat info dicts, not good if you depend on them
+ """
+ def __init__(self, env, max_random_steps, on_startup=True, every_episode=False):
+ gym.Wrapper.__init__(self, env)
+ self.on_startup = on_startup
+ self.every_episode = every_episode
+ self.random_steps = max_random_steps
+ self.last_obs = None
+ if on_startup:
+ self.some_random_steps()
+
+ def some_random_steps(self):
+ self.last_obs = self.env.reset()
+ n = np.random.randint(self.random_steps)
+ #print("running for random %i frames" % n)
+ for _ in range(n):
+ self.last_obs, _, done, _ = self.env.step(self.env.action_space.sample())
+ if done: self.last_obs = self.env.reset()
+
+ def reset(self):
+ return self.last_obs
+
+ def step(self, a):
+ self.last_obs, rew, done, info = self.env.step(a)
+ if done:
+ self.last_obs = self.env.reset()
+ if self.every_episode:
+ self.some_random_steps()
+ return self.last_obs, rew, done, info
+
+def make_retro(*, game, state=None, max_episode_steps=4500, **kwargs):
+ import retro
+ if state is None:
+ state = retro.State.DEFAULT
+ env = retro.make(game, state, **kwargs)
+ env = StochasticFrameSkip(env, n=4, stickprob=0.25)
+ if max_episode_steps is not None:
+ env = TimeLimit(env, max_episode_steps=max_episode_steps)
+ return env
+
+def wrap_deepmind_retro(env, scale=True, frame_stack=4):
+ """
+ Configure environment for retro games, using config similar to DeepMind-style Atari in wrap_deepmind
+ """
+ env = WarpFrame(env)
+ env = ClipRewardEnv(env)
+ if frame_stack > 1:
+ env = FrameStack(env, frame_stack)
+ if scale:
+ env = ScaledFloatFrame(env)
+ return env
+
+class SonicDiscretizer(gym.ActionWrapper):
+ """
+ Wrap a gym-retro environment and make it use discrete
+ actions for the Sonic game.
+ """
+ def __init__(self, env):
+ super(SonicDiscretizer, self).__init__(env)
+ buttons = ["B", "A", "MODE", "START", "UP", "DOWN", "LEFT", "RIGHT", "C", "Y", "X", "Z"]
+ actions = [['LEFT'], ['RIGHT'], ['LEFT', 'DOWN'], ['RIGHT', 'DOWN'], ['DOWN'],
+ ['DOWN', 'B'], ['B']]
+ self._actions = []
+ for action in actions:
+ arr = np.array([False] * 12)
+ for button in action:
+ arr[buttons.index(button)] = True
+ self._actions.append(arr)
+ self.action_space = gym.spaces.Discrete(len(self._actions))
+
+ def action(self, a): # pylint: disable=W0221
+ return self._actions[a].copy()
+
+class RewardScaler(gym.RewardWrapper):
+ """
+ Bring rewards to a reasonable scale for PPO.
+ This is incredibly important and effects performance
+ drastically.
+ """
+ def __init__(self, env, scale=0.01):
+ super(RewardScaler, self).__init__(env)
+ self.scale = scale
+
+ def reward(self, reward):
+ return reward * self.scale
+
+class AllowBacktracking(gym.Wrapper):
+ """
+ Use deltas in max(X) as the reward, rather than deltas
+ in X. This way, agents are not discouraged too heavily
+ from exploring backwards if there is no way to advance
+ head-on in the level.
+ """
+ def __init__(self, env):
+ super(AllowBacktracking, self).__init__(env)
+ self._cur_x = 0
+ self._max_x = 0
+
+ def reset(self, **kwargs): # pylint: disable=E0202
+ self._cur_x = 0
+ self._max_x = 0
+ return self.env.reset(**kwargs)
+
+ def step(self, action): # pylint: disable=E0202
+ obs, rew, done, info = self.env.step(action)
+ self._cur_x += rew
+ rew = max(0, self._cur_x - self._max_x)
+ self._max_x = max(self._max_x, self._cur_x)
+ return obs, rew, done, info
diff --git a/baselines/baselines/common/runners.py b/baselines/baselines/common/runners.py
new file mode 100644
index 0000000000000000000000000000000000000000..c30e322d940ca0d403290339805a9b2546c29bf5
--- /dev/null
+++ b/baselines/baselines/common/runners.py
@@ -0,0 +1,19 @@
+import numpy as np
+from abc import ABC, abstractmethod
+
+class AbstractEnvRunner(ABC):
+ def __init__(self, *, env, model, nsteps):
+ self.env = env
+ self.model = model
+ self.nenv = nenv = env.num_envs if hasattr(env, 'num_envs') else 1
+ self.batch_ob_shape = (nenv*nsteps,) + env.observation_space.shape
+ self.obs = np.zeros((nenv,) + env.observation_space.shape, dtype=env.observation_space.dtype.name)
+ self.obs[:] = env.reset()
+ self.nsteps = nsteps
+ self.states = model.initial_state
+ self.dones = [False for _ in range(nenv)]
+
+ @abstractmethod
+ def run(self):
+ raise NotImplementedError
+
diff --git a/baselines/baselines/common/running_mean_std.py b/baselines/baselines/common/running_mean_std.py
new file mode 100644
index 0000000000000000000000000000000000000000..61324c7449ba4ceaf57fc80249bbfbe6a364f186
--- /dev/null
+++ b/baselines/baselines/common/running_mean_std.py
@@ -0,0 +1,31 @@
+import numpy as np
+
+class RunningMeanStd(object):
+ # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
+ def __init__(self, epsilon=1e-4, shape=()):
+ self.mean = np.zeros(shape, 'float64')
+ self.var = np.ones(shape, 'float64')
+ self.count = epsilon
+
+ def update(self, x):
+ batch_mean = np.mean(x, axis=0)
+ batch_var = np.var(x, axis=0)
+ batch_count = x.shape[0]
+ self.update_from_moments(batch_mean, batch_var, batch_count)
+
+ def update_from_moments(self, batch_mean, batch_var, batch_count):
+ self.mean, self.var, self.count = update_mean_var_count_from_moments(
+ self.mean, self.var, self.count, batch_mean, batch_var, batch_count)
+
+def update_mean_var_count_from_moments(mean, var, count, batch_mean, batch_var, batch_count):
+ delta = batch_mean - mean
+ tot_count = count + batch_count
+
+ new_mean = mean + delta * batch_count / tot_count
+ m_a = var * count
+ m_b = batch_var * batch_count
+ M2 = m_a + m_b + np.square(delta) * count * batch_count / tot_count
+ new_var = M2 / tot_count
+ new_count = tot_count
+
+ return new_mean, new_var, new_count
diff --git a/baselines/baselines/common/schedules.py b/baselines/baselines/common/schedules.py
new file mode 100644
index 0000000000000000000000000000000000000000..9dfff50f950f396945e4602defc8d6e0066f622b
--- /dev/null
+++ b/baselines/baselines/common/schedules.py
@@ -0,0 +1,99 @@
+"""This file is used for specifying various schedules that evolve over
+time throughout the execution of the algorithm, such as:
+ - learning rate for the optimizer
+ - exploration epsilon for the epsilon greedy exploration strategy
+ - beta parameter for beta parameter in prioritized replay
+
+Each schedule has a function `value(t)` which returns the current value
+of the parameter given the timestep t of the optimization procedure.
+"""
+
+
+class Schedule(object):
+ def value(self, t):
+ """Value of the schedule at time t"""
+ raise NotImplementedError()
+
+
+class ConstantSchedule(object):
+ def __init__(self, value):
+ """Value remains constant over time.
+
+ Parameters
+ ----------
+ value: float
+ Constant value of the schedule
+ """
+ self._v = value
+
+ def value(self, t):
+ """See Schedule.value"""
+ return self._v
+
+
+def linear_interpolation(l, r, alpha):
+ return l + alpha * (r - l)
+
+
+class PiecewiseSchedule(object):
+ def __init__(self, endpoints, interpolation=linear_interpolation, outside_value=None):
+ """Piecewise schedule.
+
+ endpoints: [(int, int)]
+ list of pairs `(time, value)` meanining that schedule should output
+ `value` when `t==time`. All the values for time must be sorted in
+ an increasing order. When t is between two times, e.g. `(time_a, value_a)`
+ and `(time_b, value_b)`, such that `time_a <= t < time_b` then value outputs
+ `interpolation(value_a, value_b, alpha)` where alpha is a fraction of
+ time passed between `time_a` and `time_b` for time `t`.
+ interpolation: lambda float, float, float: float
+ a function that takes value to the left and to the right of t according
+ to the `endpoints`. Alpha is the fraction of distance from left endpoint to
+ right endpoint that t has covered. See linear_interpolation for example.
+ outside_value: float
+ if the value is requested outside of all the intervals sepecified in
+ `endpoints` this value is returned. If None then AssertionError is
+ raised when outside value is requested.
+ """
+ idxes = [e[0] for e in endpoints]
+ assert idxes == sorted(idxes)
+ self._interpolation = interpolation
+ self._outside_value = outside_value
+ self._endpoints = endpoints
+
+ def value(self, t):
+ """See Schedule.value"""
+ for (l_t, l), (r_t, r) in zip(self._endpoints[:-1], self._endpoints[1:]):
+ if l_t <= t and t < r_t:
+ alpha = float(t - l_t) / (r_t - l_t)
+ return self._interpolation(l, r, alpha)
+
+ # t does not belong to any of the pieces, so doom.
+ assert self._outside_value is not None
+ return self._outside_value
+
+
+class LinearSchedule(object):
+ def __init__(self, schedule_timesteps, final_p, initial_p=1.0):
+ """Linear interpolation between initial_p and final_p over
+ schedule_timesteps. After this many timesteps pass final_p is
+ returned.
+
+ Parameters
+ ----------
+ schedule_timesteps: int
+ Number of timesteps for which to linearly anneal initial_p
+ to final_p
+ initial_p: float
+ initial output value
+ final_p: float
+ final output value
+ """
+ self.schedule_timesteps = schedule_timesteps
+ self.final_p = final_p
+ self.initial_p = initial_p
+
+ def value(self, t):
+ """See Schedule.value"""
+ fraction = min(float(t) / self.schedule_timesteps, 1.0)
+ return self.initial_p + fraction * (self.final_p - self.initial_p)
diff --git a/baselines/baselines/common/segment_tree.py b/baselines/baselines/common/segment_tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb386ecdb5bbc1d2c6a4821d29920fc8dcb71adf
--- /dev/null
+++ b/baselines/baselines/common/segment_tree.py
@@ -0,0 +1,145 @@
+import operator
+
+
+class SegmentTree(object):
+ def __init__(self, capacity, operation, neutral_element):
+ """Build a Segment Tree data structure.
+
+ https://en.wikipedia.org/wiki/Segment_tree
+
+ Can be used as regular array, but with two
+ important differences:
+
+ a) setting item's value is slightly slower.
+ It is O(lg capacity) instead of O(1).
+ b) user has access to an efficient ( O(log segment size) )
+ `reduce` operation which reduces `operation` over
+ a contiguous subsequence of items in the array.
+
+ Paramters
+ ---------
+ capacity: int
+ Total size of the array - must be a power of two.
+ operation: lambda obj, obj -> obj
+ and operation for combining elements (eg. sum, max)
+ must form a mathematical group together with the set of
+ possible values for array elements (i.e. be associative)
+ neutral_element: obj
+ neutral element for the operation above. eg. float('-inf')
+ for max and 0 for sum.
+ """
+ assert capacity > 0 and capacity & (capacity - 1) == 0, "capacity must be positive and a power of 2."
+ self._capacity = capacity
+ self._value = [neutral_element for _ in range(2 * capacity)]
+ self._operation = operation
+
+ def _reduce_helper(self, start, end, node, node_start, node_end):
+ if start == node_start and end == node_end:
+ return self._value[node]
+ mid = (node_start + node_end) // 2
+ if end <= mid:
+ return self._reduce_helper(start, end, 2 * node, node_start, mid)
+ else:
+ if mid + 1 <= start:
+ return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end)
+ else:
+ return self._operation(
+ self._reduce_helper(start, mid, 2 * node, node_start, mid),
+ self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end)
+ )
+
+ def reduce(self, start=0, end=None):
+ """Returns result of applying `self.operation`
+ to a contiguous subsequence of the array.
+
+ self.operation(arr[start], operation(arr[start+1], operation(... arr[end])))
+
+ Parameters
+ ----------
+ start: int
+ beginning of the subsequence
+ end: int
+ end of the subsequences
+
+ Returns
+ -------
+ reduced: obj
+ result of reducing self.operation over the specified range of array elements.
+ """
+ if end is None:
+ end = self._capacity
+ if end < 0:
+ end += self._capacity
+ end -= 1
+ return self._reduce_helper(start, end, 1, 0, self._capacity - 1)
+
+ def __setitem__(self, idx, val):
+ # index of the leaf
+ idx += self._capacity
+ self._value[idx] = val
+ idx //= 2
+ while idx >= 1:
+ self._value[idx] = self._operation(
+ self._value[2 * idx],
+ self._value[2 * idx + 1]
+ )
+ idx //= 2
+
+ def __getitem__(self, idx):
+ assert 0 <= idx < self._capacity
+ return self._value[self._capacity + idx]
+
+
+class SumSegmentTree(SegmentTree):
+ def __init__(self, capacity):
+ super(SumSegmentTree, self).__init__(
+ capacity=capacity,
+ operation=operator.add,
+ neutral_element=0.0
+ )
+
+ def sum(self, start=0, end=None):
+ """Returns arr[start] + ... + arr[end]"""
+ return super(SumSegmentTree, self).reduce(start, end)
+
+ def find_prefixsum_idx(self, prefixsum):
+ """Find the highest index `i` in the array such that
+ sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum
+
+ if array values are probabilities, this function
+ allows to sample indexes according to the discrete
+ probability efficiently.
+
+ Parameters
+ ----------
+ perfixsum: float
+ upperbound on the sum of array prefix
+
+ Returns
+ -------
+ idx: int
+ highest index satisfying the prefixsum constraint
+ """
+ assert 0 <= prefixsum <= self.sum() + 1e-5
+ idx = 1
+ while idx < self._capacity: # while non-leaf
+ if self._value[2 * idx] > prefixsum:
+ idx = 2 * idx
+ else:
+ prefixsum -= self._value[2 * idx]
+ idx = 2 * idx + 1
+ return idx - self._capacity
+
+
+class MinSegmentTree(SegmentTree):
+ def __init__(self, capacity):
+ super(MinSegmentTree, self).__init__(
+ capacity=capacity,
+ operation=min,
+ neutral_element=float('inf')
+ )
+
+ def min(self, start=0, end=None):
+ """Returns min(arr[start], ..., arr[end])"""
+
+ return super(MinSegmentTree, self).reduce(start, end)
diff --git a/baselines/baselines/common/tests/test_with_mpi.py b/baselines/baselines/common/tests/test_with_mpi.py
new file mode 100644
index 0000000000000000000000000000000000000000..9388078feedb89d6fd9173320e04697503988797
--- /dev/null
+++ b/baselines/baselines/common/tests/test_with_mpi.py
@@ -0,0 +1,38 @@
+import os
+import sys
+import subprocess
+import cloudpickle
+import base64
+import pytest
+from functools import wraps
+
+try:
+ from mpi4py import MPI
+except ImportError:
+ MPI = None
+
+def with_mpi(nproc=2, timeout=30, skip_if_no_mpi=True):
+ def outer_thunk(fn):
+ @wraps(fn)
+ def thunk(*args, **kwargs):
+ serialized_fn = base64.b64encode(cloudpickle.dumps(lambda: fn(*args, **kwargs)))
+ subprocess.check_call([
+ 'mpiexec','-n', str(nproc),
+ sys.executable,
+ '-m', 'baselines.common.tests.test_with_mpi',
+ serialized_fn
+ ], env=os.environ, timeout=timeout)
+
+ if skip_if_no_mpi:
+ return pytest.mark.skipif(MPI is None, reason="MPI not present")(thunk)
+ else:
+ return thunk
+
+ return outer_thunk
+
+
+if __name__ == '__main__':
+ if len(sys.argv) > 1:
+ fn = cloudpickle.loads(base64.b64decode(sys.argv[1]))
+ assert callable(fn)
+ fn()
diff --git a/baselines/baselines/common/tf_util.py b/baselines/baselines/common/tf_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..db1076c89f42a9e2906453e573b83b21a87db66e
--- /dev/null
+++ b/baselines/baselines/common/tf_util.py
@@ -0,0 +1,199 @@
+import numpy as np
+import tensorflow as tf # pylint: ignore-module
+import copy
+
+def switch(condition, then_expression, else_expression):
+ """Switches between two operations depending on a scalar value (int or bool).
+ Note that both `then_expression` and `else_expression`
+ should be symbolic tensors of the *same shape*.
+
+ # Arguments
+ condition: scalar tensor.
+ then_expression: TensorFlow operation.
+ else_expression: TensorFlow operation.
+ """
+ x_shape = copy.copy(then_expression.get_shape())
+ x = tf.cond(tf.cast(condition, 'bool'),
+ lambda: then_expression,
+ lambda: else_expression)
+ x.set_shape(x_shape)
+ return x
+
+# ================================================================
+# Extras
+# ================================================================
+
+def lrelu(x, leak=0.2):
+ f1 = 0.5 * (1 + leak)
+ f2 = 0.5 * (1 - leak)
+ return f1 * x + f2 * abs(x)
+
+# ================================================================
+# Mathematical utils
+# ================================================================
+
+def huber_loss(x, delta=1.0):
+ """Reference: https://en.wikipedia.org/wiki/Huber_loss"""
+ return tf.where(
+ tf.abs(x) < delta,
+ tf.square(x) * 0.5,
+ delta * (tf.abs(x) - 0.5 * delta)
+ )
+
+# ================================================================
+# Model components
+# ================================================================
+
+def normc_initializer(std=1.0, axis=0):
+ def _initializer(shape, dtype=None, partition_info=None): # pylint: disable=W0613
+ out = np.random.randn(*shape).astype(dtype.as_numpy_dtype)
+ out *= std / np.sqrt(np.square(out).sum(axis=axis, keepdims=True))
+ return tf.constant(out)
+ return _initializer
+
+def conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad="SAME", dtype=tf.float32, collections=None,
+ summary_tag=None):
+ with tf.variable_scope(name):
+ stride_shape = [1, stride[0], stride[1], 1]
+ filter_shape = [filter_size[0], filter_size[1], int(x.get_shape()[3]), num_filters]
+
+ # there are "num input feature maps * filter height * filter width"
+ # inputs to each hidden unit
+ fan_in = intprod(filter_shape[:3])
+ # each unit in the lower layer receives a gradient from:
+ # "num output feature maps * filter height * filter width" /
+ # pooling size
+ fan_out = intprod(filter_shape[:2]) * num_filters
+ # initialize weights with random weights
+ w_bound = np.sqrt(6. / (fan_in + fan_out))
+
+ w = tf.get_variable("W", filter_shape, dtype, tf.random_uniform_initializer(-w_bound, w_bound),
+ collections=collections)
+ b = tf.get_variable("b", [1, 1, 1, num_filters], initializer=tf.zeros_initializer(),
+ collections=collections)
+
+ if summary_tag is not None:
+ tf.summary.image(summary_tag,
+ tf.transpose(tf.reshape(w, [filter_size[0], filter_size[1], -1, 1]),
+ [2, 0, 1, 3]),
+ max_images=10)
+
+ return tf.nn.conv2d(x, w, stride_shape, pad) + b
+
+# ================================================================
+# Flat vectors
+# ================================================================
+
+def var_shape(x):
+ out = x.get_shape().as_list()
+ assert all(isinstance(a, int) for a in out), \
+ "shape function assumes that shape is fully known"
+ return out
+
+def numel(x):
+ return intprod(var_shape(x))
+
+def intprod(x):
+ return int(np.prod(x))
+
+def flatgrad(grads, var_list, clip_norm=None):
+ if clip_norm is not None:
+ grads = [tf.clip_by_norm(grad, clip_norm=clip_norm) for grad in grads]
+ return tf.concat(axis=0, values=[
+ tf.reshape(grad if grad is not None else tf.zeros_like(v), [numel(v)])
+ for (v, grad) in zip(var_list, grads)
+ ])
+
+class SetFromFlat(object):
+ def __init__(self, var_list, dtype=tf.float32):
+ self.shapes = list(map(var_shape, var_list))
+ self.total_size = np.sum([intprod(shape) for shape in self.shapes])
+ self.var_list = var_list
+
+ def __call__(self, theta):
+ start = 0
+ for (shape, v) in zip(self.shapes, self.var_list):
+ size = intprod(shape)
+ v.assign(tf.reshape(theta[start:start + size], shape))
+ start += size
+
+class GetFlat(object):
+ def __init__(self, var_list):
+ self.var_list = var_list
+
+ def __call__(self):
+ return tf.concat(axis=0, values=[tf.reshape(v, [numel(v)]) for v in self.var_list]).numpy()
+
+def flattenallbut0(x):
+ return tf.reshape(x, [-1, intprod(x.get_shape().as_list()[1:])])
+
+
+# ================================================================
+# Shape adjustment for feeding into tf tensors
+# ================================================================
+def adjust_shape(input_tensor, data):
+ '''
+ adjust shape of the data to the shape of the tensor if possible.
+ If shape is incompatible, AssertionError is thrown
+
+ Parameters:
+ input_tensor tensorflow input tensor
+
+ data input data to be (potentially) reshaped to be fed into input
+
+ Returns:
+ reshaped data
+ '''
+
+ if not isinstance(data, np.ndarray) and not isinstance(data, list):
+ return data
+ if isinstance(data, list):
+ data = np.array(data)
+
+ input_shape = [x or -1 for x in input_tensor.shape.as_list()]
+
+ assert _check_shape(input_shape, data.shape), \
+ 'Shape of data {} is not compatible with shape of the input {}'.format(data.shape, input_shape)
+
+ return np.reshape(data, input_shape)
+
+
+def _check_shape(input_shape, data_shape):
+ ''' check if two shapes are compatible (i.e. differ only by dimensions of size 1, or by the batch dimension)'''
+
+ squeezed_input_shape = _squeeze_shape(input_shape)
+ squeezed_data_shape = _squeeze_shape(data_shape)
+
+ for i, s_data in enumerate(squeezed_data_shape):
+ s_input = squeezed_input_shape[i]
+ if s_input != -1 and s_data != s_input:
+ return False
+
+ return True
+
+
+def _squeeze_shape(shape):
+ return [x for x in shape if x != 1]
+
+# ================================================================
+# Tensorboard interfacing
+# ================================================================
+
+def launch_tensorboard_in_background(log_dir):
+ '''
+ To log the Tensorflow graph when using rl-algs
+ algorithms, you can run the following code
+ in your main script:
+ import threading, time
+ def start_tensorboard(session):
+ time.sleep(10) # Wait until graph is setup
+ tb_path = osp.join(logger.get_dir(), 'tb')
+ summary_writer = tf.summary.FileWriter(tb_path, graph=session.graph)
+ summary_op = tf.summary.merge_all()
+ launch_tensorboard_in_background(tb_path)
+ session = tf.get_default_session()
+ t = threading.Thread(target=start_tensorboard, args=([session]))
+ t.start()
+ '''
+ import subprocess
+ subprocess.Popen(['tensorboard', '--logdir', log_dir])
diff --git a/baselines/baselines/common/tile_images.py b/baselines/baselines/common/tile_images.py
new file mode 100644
index 0000000000000000000000000000000000000000..929da8994a974fbfe6065a44762fc1db3f5b36d3
--- /dev/null
+++ b/baselines/baselines/common/tile_images.py
@@ -0,0 +1,23 @@
+import numpy as np
+
+def tile_images(img_nhwc):
+ """
+ Tile N images into one big PxQ image
+ (P,Q) are chosen to be as close as possible, and if N
+ is square, then P=Q.
+
+ input: img_nhwc, list or array of images, ndim=4 once turned into array
+ n = batch index, h = height, w = width, c = channel
+ returns:
+ bigim_HWc, ndarray with ndim=3
+ """
+ img_nhwc = np.asarray(img_nhwc)
+ N, h, w, c = img_nhwc.shape
+ H = int(np.ceil(np.sqrt(N)))
+ W = int(np.ceil(float(N)/H))
+ img_nhwc = np.array(list(img_nhwc) + [img_nhwc[0]*0 for _ in range(N, H*W)])
+ img_HWhwc = img_nhwc.reshape(H, W, h, w, c)
+ img_HhWwc = img_HWhwc.transpose(0, 2, 1, 3, 4)
+ img_Hh_Ww_c = img_HhWwc.reshape(H*h, W*w, c)
+ return img_Hh_Ww_c
+
diff --git a/baselines/baselines/common/vec_env/__init__.py b/baselines/baselines/common/vec_env/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e4ef55299b4dfc220f887f68a4c86ede30d3cdc
--- /dev/null
+++ b/baselines/baselines/common/vec_env/__init__.py
@@ -0,0 +1,10 @@
+from .vec_env import AlreadySteppingError, NotSteppingError, VecEnv, VecEnvWrapper, VecEnvObservationWrapper, CloudpickleWrapper
+from .dummy_vec_env import DummyVecEnv
+from .shmem_vec_env import ShmemVecEnv
+from .subproc_vec_env import SubprocVecEnv
+from .vec_frame_stack import VecFrameStack
+from .vec_monitor import VecMonitor
+from .vec_normalize import VecNormalize
+from .vec_remove_dict_obs import VecExtractDictObs
+
+__all__ = ['AlreadySteppingError', 'NotSteppingError', 'VecEnv', 'VecEnvWrapper', 'VecEnvObservationWrapper', 'CloudpickleWrapper', 'DummyVecEnv', 'ShmemVecEnv', 'SubprocVecEnv', 'VecFrameStack', 'VecMonitor', 'VecNormalize', 'VecExtractDictObs']
diff --git a/baselines/baselines/common/vec_env/dummy_vec_env.py b/baselines/baselines/common/vec_env/dummy_vec_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..adcbce806509ac7c19afd91e2f9b8b88ce032295
--- /dev/null
+++ b/baselines/baselines/common/vec_env/dummy_vec_env.py
@@ -0,0 +1,81 @@
+import numpy as np
+from .vec_env import VecEnv
+from .util import copy_obs_dict, dict_to_obs, obs_space_info
+
+class DummyVecEnv(VecEnv):
+ """
+ VecEnv that does runs multiple environments sequentially, that is,
+ the step and reset commands are send to one environment at a time.
+ Useful when debugging and when num_env == 1 (in the latter case,
+ avoids communication overhead)
+ """
+ def __init__(self, env_fns):
+ """
+ Arguments:
+
+ env_fns: iterable of callables functions that build environments
+ """
+ self.envs = [fn() for fn in env_fns]
+ env = self.envs[0]
+ VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
+ obs_space = env.observation_space
+ self.keys, shapes, dtypes = obs_space_info(obs_space)
+
+ self.buf_obs = { k: np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]) for k in self.keys }
+ self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool)
+ self.buf_rews = np.zeros((self.num_envs,), dtype=np.float32)
+ self.buf_infos = [{} for _ in range(self.num_envs)]
+ self.actions = None
+ self.spec = self.envs[0].spec
+
+ def step_async(self, actions):
+ listify = True
+ try:
+ if len(actions) == self.num_envs:
+ listify = False
+ except TypeError:
+ pass
+
+ if not listify:
+ self.actions = actions
+ else:
+ assert self.num_envs == 1, "actions {} is either not a list or has a wrong size - cannot match to {} environments".format(actions, self.num_envs)
+ self.actions = [actions]
+
+ def step_wait(self):
+ for e in range(self.num_envs):
+ action = self.actions[e]
+ # if isinstance(self.envs[e].action_space, spaces.Discrete):
+ # action = int(action)
+
+ obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(action)
+ if self.buf_dones[e]:
+ obs = self.envs[e].reset()
+ self._save_obs(e, obs)
+ return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones),
+ self.buf_infos.copy())
+
+ def reset(self):
+ for e in range(self.num_envs):
+ obs = self.envs[e].reset()
+ self._save_obs(e, obs)
+ return self._obs_from_buf()
+
+ def _save_obs(self, e, obs):
+ for k in self.keys:
+ if k is None:
+ self.buf_obs[k][e] = obs
+ else:
+ self.buf_obs[k][e] = obs[k]
+
+ def _obs_from_buf(self):
+ return dict_to_obs(copy_obs_dict(self.buf_obs))
+
+ def get_images(self):
+ return [env.render(mode='rgb_array') for env in self.envs]
+
+ def render(self, mode='human'):
+ if self.num_envs == 1:
+ return self.envs[0].render(mode=mode)
+ else:
+ return super().render(mode=mode)
diff --git a/baselines/baselines/common/vec_env/shmem_vec_env.py b/baselines/baselines/common/vec_env/shmem_vec_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..343ef94a348ef2872eab9d56aea1add48e4d6fea
--- /dev/null
+++ b/baselines/baselines/common/vec_env/shmem_vec_env.py
@@ -0,0 +1,139 @@
+"""
+An interface for asynchronous vectorized environments.
+"""
+
+import multiprocessing as mp
+import numpy as np
+from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
+import ctypes
+from baselines import logger
+
+from .util import dict_to_obs, obs_space_info, obs_to_dict
+
+_NP_TO_CT = {np.float32: ctypes.c_float,
+ np.int32: ctypes.c_int32,
+ np.int8: ctypes.c_int8,
+ np.uint8: ctypes.c_char,
+ np.bool: ctypes.c_bool}
+
+
+class ShmemVecEnv(VecEnv):
+ """
+ Optimized version of SubprocVecEnv that uses shared variables to communicate observations.
+ """
+
+ def __init__(self, env_fns, spaces=None, context='spawn'):
+ """
+ If you don't specify observation_space, we'll have to create a dummy
+ environment to get it.
+ """
+ ctx = mp.get_context(context)
+ if spaces:
+ observation_space, action_space = spaces
+ else:
+ logger.log('Creating dummy env object to get spaces')
+ with logger.scoped_configure(format_strs=[]):
+ dummy = env_fns[0]()
+ observation_space, action_space = dummy.observation_space, dummy.action_space
+ dummy.close()
+ del dummy
+ VecEnv.__init__(self, len(env_fns), observation_space, action_space)
+ self.obs_keys, self.obs_shapes, self.obs_dtypes = obs_space_info(observation_space)
+ self.obs_bufs = [
+ {k: ctx.Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys}
+ for _ in env_fns]
+ self.parent_pipes = []
+ self.procs = []
+ with clear_mpi_env_vars():
+ for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
+ wrapped_fn = CloudpickleWrapper(env_fn)
+ parent_pipe, child_pipe = ctx.Pipe()
+ proc = ctx.Process(target=_subproc_worker,
+ args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys))
+ proc.daemon = True
+ self.procs.append(proc)
+ self.parent_pipes.append(parent_pipe)
+ proc.start()
+ child_pipe.close()
+ self.waiting_step = False
+ self.viewer = None
+
+ def reset(self):
+ if self.waiting_step:
+ logger.warn('Called reset() while waiting for the step to complete')
+ self.step_wait()
+ for pipe in self.parent_pipes:
+ pipe.send(('reset', None))
+ return self._decode_obses([pipe.recv() for pipe in self.parent_pipes])
+
+ def step_async(self, actions):
+ assert len(actions) == len(self.parent_pipes)
+ for pipe, act in zip(self.parent_pipes, actions):
+ pipe.send(('step', act))
+
+ def step_wait(self):
+ outs = [pipe.recv() for pipe in self.parent_pipes]
+ obs, rews, dones, infos = zip(*outs)
+ return self._decode_obses(obs), np.array(rews), np.array(dones), infos
+
+ def close_extras(self):
+ if self.waiting_step:
+ self.step_wait()
+ for pipe in self.parent_pipes:
+ pipe.send(('close', None))
+ for pipe in self.parent_pipes:
+ pipe.recv()
+ pipe.close()
+ for proc in self.procs:
+ proc.join()
+
+ def get_images(self, mode='human'):
+ for pipe in self.parent_pipes:
+ pipe.send(('render', None))
+ return [pipe.recv() for pipe in self.parent_pipes]
+
+ def _decode_obses(self, obs):
+ result = {}
+ for k in self.obs_keys:
+
+ bufs = [b[k] for b in self.obs_bufs]
+ o = [np.frombuffer(b.get_obj(), dtype=self.obs_dtypes[k]).reshape(self.obs_shapes[k]) for b in bufs]
+ result[k] = np.array(o)
+ return dict_to_obs(result)
+
+
+def _subproc_worker(pipe, parent_pipe, env_fn_wrapper, obs_bufs, obs_shapes, obs_dtypes, keys):
+ """
+ Control a single environment instance using IPC and
+ shared memory.
+ """
+ def _write_obs(maybe_dict_obs):
+ flatdict = obs_to_dict(maybe_dict_obs)
+ for k in keys:
+ dst = obs_bufs[k].get_obj()
+ dst_np = np.frombuffer(dst, dtype=obs_dtypes[k]).reshape(obs_shapes[k]) # pylint: disable=W0212
+ np.copyto(dst_np, flatdict[k])
+
+ env = env_fn_wrapper.x()
+ parent_pipe.close()
+ try:
+ while True:
+ cmd, data = pipe.recv()
+ if cmd == 'reset':
+ pipe.send(_write_obs(env.reset()))
+ elif cmd == 'step':
+ obs, reward, done, info = env.step(data)
+ if done:
+ obs = env.reset()
+ pipe.send((_write_obs(obs), reward, done, info))
+ elif cmd == 'render':
+ pipe.send(env.render(mode='rgb_array'))
+ elif cmd == 'close':
+ pipe.send(None)
+ break
+ else:
+ raise RuntimeError('Got unrecognized cmd %s' % cmd)
+ except KeyboardInterrupt:
+ print('ShmemVecEnv worker: got KeyboardInterrupt')
+ finally:
+ env.close()
diff --git a/baselines/baselines/common/vec_env/subproc_vec_env.py b/baselines/baselines/common/vec_env/subproc_vec_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..a36a7af1b1a5914e05133c4d772f1a2c8d36bb0d
--- /dev/null
+++ b/baselines/baselines/common/vec_env/subproc_vec_env.py
@@ -0,0 +1,117 @@
+import multiprocessing as mp
+
+import numpy as np
+from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
+
+
+def worker(remote, parent_remote, env_fn_wrapper):
+ parent_remote.close()
+ env = env_fn_wrapper.x()
+ try:
+ while True:
+ cmd, data = remote.recv()
+ if cmd == 'step':
+ ob, reward, done, info = env.step(data)
+ if done:
+ ob = env.reset()
+ remote.send((ob, reward, done, info))
+ elif cmd == 'reset':
+ ob = env.reset()
+ remote.send(ob)
+ elif cmd == 'render':
+ remote.send(env.render(mode='rgb_array'))
+ elif cmd == 'close':
+ remote.close()
+ break
+ elif cmd == 'get_spaces_spec':
+ remote.send((env.observation_space, env.action_space, env.spec))
+ else:
+ raise NotImplementedError
+ except KeyboardInterrupt:
+ print('SubprocVecEnv worker: got KeyboardInterrupt')
+ finally:
+ env.close()
+
+
+class SubprocVecEnv(VecEnv):
+ """
+ VecEnv that runs multiple environments in parallel in subproceses and communicates with them via pipes.
+ Recommended to use when num_envs > 1 and step() can be a bottleneck.
+ """
+ def __init__(self, env_fns, spaces=None, context='spawn'):
+ """
+ Arguments:
+
+ env_fns: iterable of callables - functions that create environments to run in subprocesses. Need to be cloud-pickleable
+ """
+ self.waiting = False
+ self.closed = False
+ nenvs = len(env_fns)
+ ctx = mp.get_context(context)
+ self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(nenvs)])
+ self.ps = [ctx.Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
+ for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
+ for p in self.ps:
+ p.daemon = True # if the main process crashes, we should not cause things to hang
+ with clear_mpi_env_vars():
+ p.start()
+ for remote in self.work_remotes:
+ remote.close()
+
+ self.remotes[0].send(('get_spaces_spec', None))
+ observation_space, action_space, self.spec = self.remotes[0].recv()
+ self.viewer = None
+ VecEnv.__init__(self, len(env_fns), observation_space, action_space)
+
+ def step_async(self, actions):
+ self._assert_not_closed()
+ for remote, action in zip(self.remotes, actions):
+ remote.send(('step', action))
+ self.waiting = True
+
+ def step_wait(self):
+ self._assert_not_closed()
+ results = [remote.recv() for remote in self.remotes]
+ self.waiting = False
+ obs, rews, dones, infos = zip(*results)
+ return _flatten_obs(obs), np.stack(rews), np.stack(dones), infos
+
+ def reset(self):
+ self._assert_not_closed()
+ for remote in self.remotes:
+ remote.send(('reset', None))
+ return _flatten_obs([remote.recv() for remote in self.remotes])
+
+ def close_extras(self):
+ self.closed = True
+ if self.waiting:
+ for remote in self.remotes:
+ remote.recv()
+ for remote in self.remotes:
+ remote.send(('close', None))
+ for p in self.ps:
+ p.join()
+
+ def get_images(self):
+ self._assert_not_closed()
+ for pipe in self.remotes:
+ pipe.send(('render', None))
+ imgs = [pipe.recv() for pipe in self.remotes]
+ return imgs
+
+ def _assert_not_closed(self):
+ assert not self.closed, "Trying to operate on a SubprocVecEnv after calling close()"
+
+ def __del__(self):
+ if not self.closed:
+ self.close()
+
+def _flatten_obs(obs):
+ assert isinstance(obs, (list, tuple))
+ assert len(obs) > 0
+
+ if isinstance(obs[0], dict):
+ keys = obs[0].keys()
+ return {k: np.stack([o[k] for o in obs]) for k in keys}
+ else:
+ return np.stack(obs)
diff --git a/baselines/baselines/common/vec_env/test_vec_env.py b/baselines/baselines/common/vec_env/test_vec_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..4feabb0816ee925fa247dd41074e26762f0ce9f0
--- /dev/null
+++ b/baselines/baselines/common/vec_env/test_vec_env.py
@@ -0,0 +1,114 @@
+"""
+Tests for asynchronous vectorized environments.
+"""
+
+import gym
+import numpy as np
+import pytest
+from .dummy_vec_env import DummyVecEnv
+from .shmem_vec_env import ShmemVecEnv
+from .subproc_vec_env import SubprocVecEnv
+from baselines.common.tests.test_with_mpi import with_mpi
+
+
+def assert_venvs_equal(venv1, venv2, num_steps):
+ """
+ Compare two environments over num_steps steps and make sure
+ that the observations produced by each are the same when given
+ the same actions.
+ """
+ assert venv1.num_envs == venv2.num_envs
+ assert venv1.observation_space.shape == venv2.observation_space.shape
+ assert venv1.observation_space.dtype == venv2.observation_space.dtype
+ assert venv1.action_space.shape == venv2.action_space.shape
+ assert venv1.action_space.dtype == venv2.action_space.dtype
+
+ try:
+ obs1, obs2 = venv1.reset(), venv2.reset()
+ assert np.array(obs1).shape == np.array(obs2).shape
+ assert np.array(obs1).shape == (venv1.num_envs,) + venv1.observation_space.shape
+ assert np.allclose(obs1, obs2)
+ venv1.action_space.seed(1337)
+ for _ in range(num_steps):
+ actions = np.array([venv1.action_space.sample() for _ in range(venv1.num_envs)])
+ for venv in [venv1, venv2]:
+ venv.step_async(actions)
+ outs1 = venv1.step_wait()
+ outs2 = venv2.step_wait()
+ for out1, out2 in zip(outs1[:3], outs2[:3]):
+ assert np.array(out1).shape == np.array(out2).shape
+ assert np.allclose(out1, out2)
+ assert list(outs1[3]) == list(outs2[3])
+ finally:
+ venv1.close()
+ venv2.close()
+
+
+@pytest.mark.parametrize('klass', (ShmemVecEnv, SubprocVecEnv))
+@pytest.mark.parametrize('dtype', ('uint8', 'float32'))
+def test_vec_env(klass, dtype): # pylint: disable=R0914
+ """
+ Test that a vectorized environment is equivalent to
+ DummyVecEnv, since DummyVecEnv is less likely to be
+ error prone.
+ """
+ num_envs = 3
+ num_steps = 100
+ shape = (3, 8)
+
+ def make_fn(seed):
+ """
+ Get an environment constructor with a seed.
+ """
+ return lambda: SimpleEnv(seed, shape, dtype)
+ fns = [make_fn(i) for i in range(num_envs)]
+ env1 = DummyVecEnv(fns)
+ env2 = klass(fns)
+ assert_venvs_equal(env1, env2, num_steps=num_steps)
+
+
+class SimpleEnv(gym.Env):
+ """
+ An environment with a pre-determined observation space
+ and RNG seed.
+ """
+
+ def __init__(self, seed, shape, dtype):
+ np.random.seed(seed)
+ self._dtype = dtype
+ self._start_obs = np.array(np.random.randint(0, 0x100, size=shape),
+ dtype=dtype)
+ self._max_steps = seed + 1
+ self._cur_obs = None
+ self._cur_step = 0
+ # this is 0xFF instead of 0x100 because the Box space includes
+ # the high end, while randint does not
+ self.action_space = gym.spaces.Box(low=0, high=0xFF, shape=shape, dtype=dtype)
+ self.observation_space = self.action_space
+
+ def step(self, action):
+ self._cur_obs += np.array(action, dtype=self._dtype)
+ self._cur_step += 1
+ done = self._cur_step >= self._max_steps
+ reward = self._cur_step / self._max_steps
+ return self._cur_obs, reward, done, {'foo': 'bar' + str(reward)}
+
+ def reset(self):
+ self._cur_obs = self._start_obs
+ self._cur_step = 0
+ return self._cur_obs
+
+ def render(self, mode=None):
+ raise NotImplementedError
+
+
+
+@with_mpi()
+def test_mpi_with_subprocvecenv():
+ shape = (2,3,4)
+ nenv = 1
+ venv = SubprocVecEnv([lambda: SimpleEnv(0, shape, 'float32')] * nenv)
+ ob = venv.reset()
+ venv.close()
+ assert ob.shape == (nenv,) + shape
+
diff --git a/baselines/baselines/common/vec_env/test_video_recorder.py b/baselines/baselines/common/vec_env/test_video_recorder.py
new file mode 100644
index 0000000000000000000000000000000000000000..363404a60f27ec0d465da9e164296a77e37ddcca
--- /dev/null
+++ b/baselines/baselines/common/vec_env/test_video_recorder.py
@@ -0,0 +1,49 @@
+"""
+Tests for asynchronous vectorized environments.
+"""
+
+import gym
+import pytest
+import os
+import glob
+import tempfile
+
+from .dummy_vec_env import DummyVecEnv
+from .shmem_vec_env import ShmemVecEnv
+from .subproc_vec_env import SubprocVecEnv
+from .vec_video_recorder import VecVideoRecorder
+
+@pytest.mark.parametrize('klass', (DummyVecEnv, ShmemVecEnv, SubprocVecEnv))
+@pytest.mark.parametrize('num_envs', (1, 4))
+@pytest.mark.parametrize('video_length', (10, 100))
+@pytest.mark.parametrize('video_interval', (1, 50))
+def test_video_recorder(klass, num_envs, video_length, video_interval):
+ """
+ Wrap an existing VecEnv with VevVideoRecorder,
+ Make (video_interval + video_length + 1) steps,
+ then check that the file is present
+ """
+
+ def make_fn():
+ env = gym.make('PongNoFrameskip-v4')
+ return env
+ fns = [make_fn for _ in range(num_envs)]
+ env = klass(fns)
+
+ with tempfile.TemporaryDirectory() as video_path:
+ env = VecVideoRecorder(env, video_path, record_video_trigger=lambda x: x % video_interval == 0, video_length=video_length)
+
+ env.reset()
+ for _ in range(video_interval + video_length + 1):
+ env.step([0] * num_envs)
+ env.close()
+
+
+ recorded_video = glob.glob(os.path.join(video_path, "*.mp4"))
+
+ # first and second step
+ assert len(recorded_video) == 2
+ # Files are not empty
+ assert all(os.stat(p).st_size != 0 for p in recorded_video)
+
+
diff --git a/baselines/baselines/common/vec_env/util.py b/baselines/baselines/common/vec_env/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..d29d8a33239fa3344254d7e6b3cb8642a1d71043
--- /dev/null
+++ b/baselines/baselines/common/vec_env/util.py
@@ -0,0 +1,59 @@
+"""
+Helpers for dealing with vectorized environments.
+"""
+
+from collections import OrderedDict
+
+import gym
+import numpy as np
+
+
+def copy_obs_dict(obs):
+ """
+ Deep-copy an observation dict.
+ """
+ return {k: np.copy(v) for k, v in obs.items()}
+
+
+def dict_to_obs(obs_dict):
+ """
+ Convert an observation dict into a raw array if the
+ original observation space was not a Dict space.
+ """
+ if set(obs_dict.keys()) == {None}:
+ return obs_dict[None]
+ return obs_dict
+
+
+def obs_space_info(obs_space):
+ """
+ Get dict-structured information about a gym.Space.
+
+ Returns:
+ A tuple (keys, shapes, dtypes):
+ keys: a list of dict keys.
+ shapes: a dict mapping keys to shapes.
+ dtypes: a dict mapping keys to dtypes.
+ """
+ if isinstance(obs_space, gym.spaces.Dict):
+ assert isinstance(obs_space.spaces, OrderedDict)
+ subspaces = obs_space.spaces
+ else:
+ subspaces = {None: obs_space}
+ keys = []
+ shapes = {}
+ dtypes = {}
+ for key, box in subspaces.items():
+ keys.append(key)
+ shapes[key] = box.shape
+ dtypes[key] = box.dtype
+ return keys, shapes, dtypes
+
+
+def obs_to_dict(obs):
+ """
+ Convert an observation into a dict.
+ """
+ if isinstance(obs, dict):
+ return obs
+ return {None: obs}
diff --git a/baselines/baselines/common/vec_env/vec_env.py b/baselines/baselines/common/vec_env/vec_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..437d15f928e2c9e7c58aed2221528f21d1bef47e
--- /dev/null
+++ b/baselines/baselines/common/vec_env/vec_env.py
@@ -0,0 +1,218 @@
+import contextlib
+import os
+from abc import ABC, abstractmethod
+
+from baselines.common.tile_images import tile_images
+
+class AlreadySteppingError(Exception):
+ """
+ Raised when an asynchronous step is running while
+ step_async() is called again.
+ """
+
+ def __init__(self):
+ msg = 'already running an async step'
+ Exception.__init__(self, msg)
+
+
+class NotSteppingError(Exception):
+ """
+ Raised when an asynchronous step is not running but
+ step_wait() is called.
+ """
+
+ def __init__(self):
+ msg = 'not running an async step'
+ Exception.__init__(self, msg)
+
+
+class VecEnv(ABC):
+ """
+ An abstract asynchronous, vectorized environment.
+ Used to batch data from multiple copies of an environment, so that
+ each observation becomes an batch of observations, and expected action is a batch of actions to
+ be applied per-environment.
+ """
+ closed = False
+ viewer = None
+
+ metadata = {
+ 'render.modes': ['human', 'rgb_array']
+ }
+
+ def __init__(self, num_envs, observation_space, action_space):
+ self.num_envs = num_envs
+ self.observation_space = observation_space
+ self.action_space = action_space
+
+ @abstractmethod
+ def reset(self):
+ """
+ Reset all the environments and return an array of
+ observations, or a dict of observation arrays.
+
+ If step_async is still doing work, that work will
+ be cancelled and step_wait() should not be called
+ until step_async() is invoked again.
+ """
+ pass
+
+ @abstractmethod
+ def step_async(self, actions):
+ """
+ Tell all the environments to start taking a step
+ with the given actions.
+ Call step_wait() to get the results of the step.
+
+ You should not call this if a step_async run is
+ already pending.
+ """
+ pass
+
+ @abstractmethod
+ def step_wait(self):
+ """
+ Wait for the step taken with step_async().
+
+ Returns (obs, rews, dones, infos):
+ - obs: an array of observations, or a dict of
+ arrays of observations.
+ - rews: an array of rewards
+ - dones: an array of "episode done" booleans
+ - infos: a sequence of info objects
+ """
+ pass
+
+ def close_extras(self):
+ """
+ Clean up the extra resources, beyond what's in this base class.
+ Only runs when not self.closed.
+ """
+ pass
+
+ def close(self):
+ if self.closed:
+ return
+ if self.viewer is not None:
+ self.viewer.close()
+ self.close_extras()
+ self.closed = True
+
+ def step(self, actions):
+ """
+ Step the environments synchronously.
+
+ This is available for backwards compatibility.
+ """
+ self.step_async(actions)
+ return self.step_wait()
+
+ def render(self, mode='human'):
+ imgs = self.get_images()
+ bigimg = tile_images(imgs)
+ if mode == 'human':
+ self.get_viewer().imshow(bigimg)
+ return self.get_viewer().isopen
+ elif mode == 'rgb_array':
+ return bigimg
+ else:
+ raise NotImplementedError
+
+ def get_images(self):
+ """
+ Return RGB images from each environment
+ """
+ raise NotImplementedError
+
+ @property
+ def unwrapped(self):
+ if isinstance(self, VecEnvWrapper):
+ return self.venv.unwrapped
+ else:
+ return self
+
+ def get_viewer(self):
+ if self.viewer is None:
+ from gym.envs.classic_control import rendering
+ self.viewer = rendering.SimpleImageViewer()
+ return self.viewer
+
+class VecEnvWrapper(VecEnv):
+ """
+ An environment wrapper that applies to an entire batch
+ of environments at once.
+ """
+
+ def __init__(self, venv, observation_space=None, action_space=None):
+ self.venv = venv
+ super().__init__(num_envs=venv.num_envs,
+ observation_space=observation_space or venv.observation_space,
+ action_space=action_space or venv.action_space)
+
+ def step_async(self, actions):
+ self.venv.step_async(actions)
+
+ @abstractmethod
+ def reset(self):
+ pass
+
+ @abstractmethod
+ def step_wait(self):
+ pass
+
+ def close(self):
+ return self.venv.close()
+
+ def render(self, mode='human'):
+ return self.venv.render(mode=mode)
+
+ def get_images(self):
+ return self.venv.get_images()
+
+class VecEnvObservationWrapper(VecEnvWrapper):
+ @abstractmethod
+ def process(self, obs):
+ pass
+
+ def reset(self):
+ obs = self.venv.reset()
+ return self.process(obs)
+
+ def step_wait(self):
+ obs, rews, dones, infos = self.venv.step_wait()
+ return self.process(obs), rews, dones, infos
+
+class CloudpickleWrapper(object):
+ """
+ Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
+ """
+
+ def __init__(self, x):
+ self.x = x
+
+ def __getstate__(self):
+ import cloudpickle
+ return cloudpickle.dumps(self.x)
+
+ def __setstate__(self, ob):
+ import pickle
+ self.x = pickle.loads(ob)
+
+
+@contextlib.contextmanager
+def clear_mpi_env_vars():
+ """
+ from mpi4py import MPI will call MPI_Init by default. If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang.
+ This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing
+ Processes.
+ """
+ removed_environment = {}
+ for k, v in list(os.environ.items()):
+ for prefix in ['OMPI_', 'PMI_']:
+ if k.startswith(prefix):
+ removed_environment[k] = v
+ del os.environ[k]
+ try:
+ yield
+ finally:
+ os.environ.update(removed_environment)
diff --git a/baselines/baselines/common/vec_env/vec_frame_stack.py b/baselines/baselines/common/vec_env/vec_frame_stack.py
new file mode 100644
index 0000000000000000000000000000000000000000..0da1a0aadacccadfb2344ab369c3202212dd0c9b
--- /dev/null
+++ b/baselines/baselines/common/vec_env/vec_frame_stack.py
@@ -0,0 +1,30 @@
+from .vec_env import VecEnvWrapper
+import numpy as np
+from gym import spaces
+
+
+class VecFrameStack(VecEnvWrapper):
+ def __init__(self, venv, nstack):
+ self.venv = venv
+ self.nstack = nstack
+ wos = venv.observation_space # wrapped ob space
+ low = np.repeat(wos.low, self.nstack, axis=-1)
+ high = np.repeat(wos.high, self.nstack, axis=-1)
+ self.stackedobs = np.zeros((venv.num_envs,) + low.shape, low.dtype)
+ observation_space = spaces.Box(low=low, high=high, dtype=venv.observation_space.dtype)
+ VecEnvWrapper.__init__(self, venv, observation_space=observation_space)
+
+ def step_wait(self):
+ obs, rews, news, infos = self.venv.step_wait()
+ self.stackedobs = np.roll(self.stackedobs, shift=-1, axis=-1)
+ for (i, new) in enumerate(news):
+ if new:
+ self.stackedobs[i] = 0
+ self.stackedobs[..., -obs.shape[-1]:] = obs
+ return self.stackedobs, rews, news, infos
+
+ def reset(self):
+ obs = self.venv.reset()
+ self.stackedobs[...] = 0
+ self.stackedobs[..., -obs.shape[-1]:] = obs
+ return self.stackedobs
diff --git a/baselines/baselines/common/vec_env/vec_monitor.py b/baselines/baselines/common/vec_env/vec_monitor.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e673783f1dcdc292ce3489703e4bf807b334dbc
--- /dev/null
+++ b/baselines/baselines/common/vec_env/vec_monitor.py
@@ -0,0 +1,49 @@
+from . import VecEnvWrapper
+from baselines.bench.monitor import ResultsWriter
+import numpy as np
+import time
+from collections import deque
+
+class VecMonitor(VecEnvWrapper):
+ def __init__(self, venv, filename=None, keep_buf=0):
+ VecEnvWrapper.__init__(self, venv)
+ self.eprets = None
+ self.eplens = None
+ self.epcount = 0
+ self.tstart = time.time()
+ if filename:
+ self.results_writer = ResultsWriter(filename, header={'t_start': self.tstart})
+ else:
+ self.results_writer = None
+ self.keep_buf = keep_buf
+ if self.keep_buf:
+ self.epret_buf = deque([], maxlen=keep_buf)
+ self.eplen_buf = deque([], maxlen=keep_buf)
+
+ def reset(self):
+ obs = self.venv.reset()
+ self.eprets = np.zeros(self.num_envs, 'f')
+ self.eplens = np.zeros(self.num_envs, 'i')
+ return obs
+
+ def step_wait(self):
+ obs, rews, dones, infos = self.venv.step_wait()
+ self.eprets += rews
+ self.eplens += 1
+ newinfos = []
+ for (i, (done, ret, eplen, info)) in enumerate(zip(dones, self.eprets, self.eplens, infos)):
+ info = info.copy()
+ if done:
+ epinfo = {'r': ret, 'l': eplen, 't': round(time.time() - self.tstart, 6)}
+ info['episode'] = epinfo
+ if self.keep_buf:
+ self.epret_buf.append(ret)
+ self.eplen_buf.append(eplen)
+ self.epcount += 1
+ self.eprets[i] = 0
+ self.eplens[i] = 0
+ if self.results_writer:
+ self.results_writer.write_row(epinfo)
+ newinfos.append(info)
+
+ return obs, rews, dones, newinfos
diff --git a/baselines/baselines/common/vec_env/vec_normalize.py b/baselines/baselines/common/vec_env/vec_normalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3255e9c372c3929ea36a4d5b367b5a53c8b3b84
--- /dev/null
+++ b/baselines/baselines/common/vec_env/vec_normalize.py
@@ -0,0 +1,43 @@
+from . import VecEnvWrapper
+from baselines.common.running_mean_std import RunningMeanStd
+import numpy as np
+
+
+class VecNormalize(VecEnvWrapper):
+ """
+ A vectorized wrapper that normalizes the observations
+ and returns from an environment.
+ """
+
+ def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8):
+ VecEnvWrapper.__init__(self, venv)
+ self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if ob else None
+ self.ret_rms = RunningMeanStd(shape=()) if ret else None
+ self.clipob = clipob
+ self.cliprew = cliprew
+ self.ret = np.zeros(self.num_envs)
+ self.gamma = gamma
+ self.epsilon = epsilon
+
+ def step_wait(self):
+ obs, rews, news, infos = self.venv.step_wait()
+ self.ret = self.ret * self.gamma + rews
+ obs = self._obfilt(obs)
+ if self.ret_rms:
+ self.ret_rms.update(self.ret)
+ rews = np.clip(rews / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew)
+ self.ret[news] = 0.
+ return obs, rews, news, infos
+
+ def _obfilt(self, obs):
+ if self.ob_rms:
+ self.ob_rms.update(obs)
+ obs = np.clip((obs - self.ob_rms.mean) / np.sqrt(self.ob_rms.var + self.epsilon), -self.clipob, self.clipob)
+ return obs
+ else:
+ return obs
+
+ def reset(self):
+ self.ret = np.zeros(self.num_envs)
+ obs = self.venv.reset()
+ return self._obfilt(obs)
diff --git a/baselines/baselines/common/vec_env/vec_remove_dict_obs.py b/baselines/baselines/common/vec_env/vec_remove_dict_obs.py
new file mode 100644
index 0000000000000000000000000000000000000000..602b949ed2c87437f326adf223e3da6fcfd07bef
--- /dev/null
+++ b/baselines/baselines/common/vec_env/vec_remove_dict_obs.py
@@ -0,0 +1,11 @@
+from .vec_env import VecEnvObservationWrapper
+
+
+class VecExtractDictObs(VecEnvObservationWrapper):
+ def __init__(self, venv, key):
+ self.key = key
+ super().__init__(venv=venv,
+ observation_space=venv.observation_space.spaces[self.key])
+
+ def process(self, obs):
+ return obs[self.key]
\ No newline at end of file
diff --git a/baselines/baselines/common/vec_env/vec_video_recorder.py b/baselines/baselines/common/vec_env/vec_video_recorder.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4e7059ff0a7d20710482d1aa8262fbcc2128671
--- /dev/null
+++ b/baselines/baselines/common/vec_env/vec_video_recorder.py
@@ -0,0 +1,89 @@
+import os
+from baselines import logger
+from baselines.common.vec_env import VecEnvWrapper
+from gym.wrappers.monitoring import video_recorder
+
+
+class VecVideoRecorder(VecEnvWrapper):
+ """
+ Wrap VecEnv to record rendered image as mp4 video.
+ """
+
+ def __init__(self, venv, directory, record_video_trigger, video_length=200):
+ """
+ # Arguments
+ venv: VecEnv to wrap
+ directory: Where to save videos
+ record_video_trigger:
+ Function that defines when to start recording.
+ The function takes the current number of step,
+ and returns whether we should start recording or not.
+ video_length: Length of recorded video
+ """
+
+ VecEnvWrapper.__init__(self, venv)
+ self.record_video_trigger = record_video_trigger
+ self.video_recorder = None
+
+ self.directory = os.path.abspath(directory)
+ if not os.path.exists(self.directory): os.mkdir(self.directory)
+
+ self.file_prefix = "vecenv"
+ self.file_infix = '{}'.format(os.getpid())
+ self.step_id = 0
+ self.video_length = video_length
+
+ self.recording = False
+ self.recorded_frames = 0
+
+ def reset(self):
+ obs = self.venv.reset()
+
+ self.start_video_recorder()
+
+ return obs
+
+ def start_video_recorder(self):
+ self.close_video_recorder()
+
+ base_path = os.path.join(self.directory, '{}.video.{}.video{:06}'.format(self.file_prefix, self.file_infix, self.step_id))
+ self.video_recorder = video_recorder.VideoRecorder(
+ env=self.venv,
+ base_path=base_path,
+ metadata={'step_id': self.step_id}
+ )
+
+ self.video_recorder.capture_frame()
+ self.recorded_frames = 1
+ self.recording = True
+
+ def _video_enabled(self):
+ return self.record_video_trigger(self.step_id)
+
+ def step_wait(self):
+ obs, rews, dones, infos = self.venv.step_wait()
+
+ self.step_id += 1
+ if self.recording:
+ self.video_recorder.capture_frame()
+ self.recorded_frames += 1
+ if self.recorded_frames > self.video_length:
+ logger.info("Saving video to ", self.video_recorder.path)
+ self.close_video_recorder()
+ elif self._video_enabled():
+ self.start_video_recorder()
+
+ return obs, rews, dones, infos
+
+ def close_video_recorder(self):
+ if self.recording:
+ self.video_recorder.close()
+ self.recording = False
+ self.recorded_frames = 0
+
+ def close(self):
+ VecEnvWrapper.close(self)
+ self.close_video_recorder()
+
+ def __del__(self):
+ self.close()
diff --git a/baselines/baselines/common/wrappers.py b/baselines/baselines/common/wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..7683d180177644f5055681d4a2a762f2976267c5
--- /dev/null
+++ b/baselines/baselines/common/wrappers.py
@@ -0,0 +1,19 @@
+import gym
+
+class TimeLimit(gym.Wrapper):
+ def __init__(self, env, max_episode_steps=None):
+ super(TimeLimit, self).__init__(env)
+ self._max_episode_steps = max_episode_steps
+ self._elapsed_steps = 0
+
+ def step(self, ac):
+ observation, reward, done, info = self.env.step(ac)
+ self._elapsed_steps += 1
+ if self._elapsed_steps >= self._max_episode_steps:
+ done = True
+ info['TimeLimit.truncated'] = True
+ return observation, reward, done, info
+
+ def reset(self, **kwargs):
+ self._elapsed_steps = 0
+ return self.env.reset(**kwargs)
\ No newline at end of file
diff --git a/baselines/baselines/ddpg/README.md b/baselines/baselines/ddpg/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ed6d23fa610769665ed5d9f8116f2f5b74dfb9a9
--- /dev/null
+++ b/baselines/baselines/ddpg/README.md
@@ -0,0 +1,5 @@
+# DDPG
+
+- Original paper: https://arxiv.org/abs/1509.02971
+- Baselines post: https://blog.openai.com/better-exploration-with-parameter-noise/
+- `python -m baselines.run --alg=ddpg --env=HalfCheetah-v2 --num_timesteps=1e6` runs the algorithm for 1M frames = 10M timesteps on a Mujoco environment. See help (`-h`) for more options.
diff --git a/baselines/baselines/ddpg/__init__.py b/baselines/baselines/ddpg/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/baselines/baselines/ddpg/ddpg.py b/baselines/baselines/ddpg/ddpg.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a2c405aef1ab33457cf8c88423bb2ac392300fb
--- /dev/null
+++ b/baselines/baselines/ddpg/ddpg.py
@@ -0,0 +1,285 @@
+import os
+import os.path as osp
+import time
+from collections import deque
+import pickle
+
+from baselines.ddpg.ddpg_learner import DDPG
+from baselines.ddpg.models import Actor, Critic
+from baselines.ddpg.memory import Memory
+from baselines.ddpg.noise import AdaptiveParamNoiseSpec, NormalActionNoise, OrnsteinUhlenbeckActionNoise
+from baselines.common import set_global_seeds
+
+from baselines import logger
+import tensorflow as tf
+import numpy as np
+
+try:
+ from mpi4py import MPI
+except ImportError:
+ MPI = None
+
+def learn(network, env,
+ seed=None,
+ total_timesteps=None,
+ nb_epochs=None, # with default settings, perform 1M steps total
+ nb_epoch_cycles=20,
+ nb_rollout_steps=100,
+ reward_scale=1.0,
+ render=False,
+ render_eval=False,
+ noise_type='adaptive-param_0.2',
+ normalize_returns=False,
+ normalize_observations=True,
+ critic_l2_reg=1e-2,
+ actor_lr=1e-4,
+ critic_lr=1e-3,
+ popart=False,
+ gamma=0.99,
+ clip_norm=None,
+ nb_train_steps=50, # per epoch cycle and MPI worker,
+ nb_eval_steps=100,
+ batch_size=64, # per MPI worker
+ tau=0.01,
+ eval_env=None,
+ param_noise_adaption_interval=50,
+ load_path=None,
+ **network_kwargs):
+
+ set_global_seeds(seed)
+
+ if total_timesteps is not None:
+ assert nb_epochs is None
+ nb_epochs = int(total_timesteps) // (nb_epoch_cycles * nb_rollout_steps)
+ else:
+ nb_epochs = 500
+
+ if MPI is not None:
+ rank = MPI.COMM_WORLD.Get_rank()
+ else:
+ rank = 0
+
+ nb_actions = env.action_space.shape[-1]
+ assert (np.abs(env.action_space.low) == env.action_space.high).all() # we assume symmetric actions.
+
+ memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
+ critic = Critic(nb_actions, ob_shape=env.observation_space.shape, network=network, **network_kwargs)
+ actor = Actor(nb_actions, ob_shape=env.observation_space.shape, network=network, **network_kwargs)
+
+ action_noise = None
+ param_noise = None
+ if noise_type is not None:
+ for current_noise_type in noise_type.split(','):
+ current_noise_type = current_noise_type.strip()
+ if current_noise_type == 'none':
+ pass
+ elif 'adaptive-param' in current_noise_type:
+ _, stddev = current_noise_type.split('_')
+ param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))
+ elif 'normal' in current_noise_type:
+ _, stddev = current_noise_type.split('_')
+ action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
+ elif 'ou' in current_noise_type:
+ _, stddev = current_noise_type.split('_')
+ action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
+ else:
+ raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
+
+ max_action = env.action_space.high
+ logger.info('scaling actions by {} before executing in env'.format(max_action))
+
+ agent = DDPG(actor, critic, memory, env.observation_space.shape, env.action_space.shape,
+ gamma=gamma, tau=tau, normalize_returns=normalize_returns, normalize_observations=normalize_observations,
+ batch_size=batch_size, action_noise=action_noise, param_noise=param_noise, critic_l2_reg=critic_l2_reg,
+ actor_lr=actor_lr, critic_lr=critic_lr, enable_popart=popart, clip_norm=clip_norm,
+ reward_scale=reward_scale)
+ logger.info('Using agent with the following configuration:')
+ logger.info(str(agent.__dict__.items()))
+
+ if load_path is not None:
+ load_path = osp.expanduser(load_path)
+ ckpt = tf.train.Checkpoint(model=agent)
+ manager = tf.train.CheckpointManager(ckpt, load_path, max_to_keep=None)
+ ckpt.restore(manager.latest_checkpoint)
+ print("Restoring from {}".format(manager.latest_checkpoint))
+
+ eval_episode_rewards_history = deque(maxlen=100)
+ episode_rewards_history = deque(maxlen=100)
+
+ # Prepare everything.
+ agent.initialize()
+
+ agent.reset()
+
+ obs = env.reset()
+ if eval_env is not None:
+ eval_obs = eval_env.reset()
+ nenvs = obs.shape[0]
+
+ episode_reward = np.zeros(nenvs, dtype = np.float32) #vector
+ episode_step = np.zeros(nenvs, dtype = int) # vector
+ episodes = 0 #scalar
+ t = 0 # scalar
+
+ epoch = 0
+
+
+
+ start_time = time.time()
+
+ epoch_episode_rewards = []
+ epoch_episode_steps = []
+ epoch_actions = []
+ epoch_qs = []
+ epoch_episodes = 0
+ for epoch in range(nb_epochs):
+ for cycle in range(nb_epoch_cycles):
+ # Perform rollouts.
+ if nenvs > 1:
+ # if simulating multiple envs in parallel, impossible to reset agent at the end of the episode in each
+ # of the environments, so resetting here instead
+ agent.reset()
+ for t_rollout in range(nb_rollout_steps):
+ # Predict next action.
+ action, q, _, _ = agent.step(tf.constant(obs), apply_noise=True, compute_Q=True)
+ action, q = action.numpy(), q.numpy()
+
+ # Execute next action.
+ if rank == 0 and render:
+ env.render()
+
+ # max_action is of dimension A, whereas action is dimension (nenvs, A) - the multiplication gets broadcasted to the batch
+ new_obs, r, done, info = env.step(max_action * action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
+ # note these outputs are batched from vecenv
+
+ t += 1
+ if rank == 0 and render:
+ env.render()
+ episode_reward += r
+ episode_step += 1
+
+ # Book-keeping.
+ epoch_actions.append(action)
+ epoch_qs.append(q)
+ agent.store_transition(obs, action, r, new_obs, done) #the batched data will be unrolled in memory.py's append.
+
+ obs = new_obs
+
+ for d in range(len(done)):
+ if done[d]:
+ # Episode done.
+ epoch_episode_rewards.append(episode_reward[d])
+ episode_rewards_history.append(episode_reward[d])
+ epoch_episode_steps.append(episode_step[d])
+ episode_reward[d] = 0.
+ episode_step[d] = 0
+ epoch_episodes += 1
+ episodes += 1
+ if nenvs == 1:
+ agent.reset()
+
+
+ # Train.
+ epoch_actor_losses = []
+ epoch_critic_losses = []
+ epoch_adaptive_distances = []
+ for t_train in range(nb_train_steps):
+ # Adapt param noise, if necessary.
+ if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:
+ batch = agent.memory.sample(batch_size=batch_size)
+ obs0 = tf.constant(batch['obs0'])
+ distance = agent.adapt_param_noise(obs0)
+ epoch_adaptive_distances.append(distance)
+
+ cl, al = agent.train()
+ epoch_critic_losses.append(cl)
+ epoch_actor_losses.append(al)
+ agent.update_target_net()
+
+ # Evaluate.
+ eval_episode_rewards = []
+ eval_qs = []
+ if eval_env is not None:
+ nenvs_eval = eval_obs.shape[0]
+ eval_episode_reward = np.zeros(nenvs_eval, dtype = np.float32)
+ for t_rollout in range(nb_eval_steps):
+ eval_action, eval_q, _, _ = agent.step(eval_obs, apply_noise=False, compute_Q=True)
+ eval_obs, eval_r, eval_done, eval_info = eval_env.step(max_action * eval_action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
+ if render_eval:
+ eval_env.render()
+ eval_episode_reward += eval_r
+
+ eval_qs.append(eval_q)
+ for d in range(len(eval_done)):
+ if eval_done[d]:
+ eval_episode_rewards.append(eval_episode_reward[d])
+ eval_episode_rewards_history.append(eval_episode_reward[d])
+ eval_episode_reward[d] = 0.0
+
+ if MPI is not None:
+ mpi_size = MPI.COMM_WORLD.Get_size()
+ else:
+ mpi_size = 1
+
+ # Log stats.
+ # XXX shouldn't call np.mean on variable length lists
+ duration = time.time() - start_time
+ stats = agent.get_stats()
+ combined_stats = stats.copy()
+ combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
+ combined_stats['rollout/return_std'] = np.std(epoch_episode_rewards)
+ combined_stats['rollout/return_history'] = np.mean(episode_rewards_history)
+ combined_stats['rollout/return_history_std'] = np.std(episode_rewards_history)
+ combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
+ combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
+ combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
+ combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
+ combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
+ combined_stats['train/param_noise_distance'] = np.mean(epoch_adaptive_distances)
+ combined_stats['total/duration'] = duration
+ combined_stats['total/steps_per_second'] = float(t) / float(duration)
+ combined_stats['total/episodes'] = episodes
+ combined_stats['rollout/episodes'] = epoch_episodes
+ combined_stats['rollout/actions_std'] = np.std(epoch_actions)
+ # Evaluation statistics.
+ if eval_env is not None:
+ combined_stats['eval/return'] = eval_episode_rewards
+ combined_stats['eval/return_history'] = np.mean(eval_episode_rewards_history)
+ combined_stats['eval/Q'] = eval_qs
+ combined_stats['eval/episodes'] = len(eval_episode_rewards)
+ def as_scalar(x):
+ if isinstance(x, np.ndarray):
+ assert x.size == 1
+ return x[0]
+ elif np.isscalar(x):
+ return x
+ else:
+ raise ValueError('expected scalar, got %s'%x)
+
+ combined_stats_sums = np.array([ np.array(x).flatten()[0] for x in combined_stats.values()])
+ if MPI is not None:
+ combined_stats_sums = MPI.COMM_WORLD.allreduce(combined_stats_sums)
+
+ combined_stats = {k : v / mpi_size for (k,v) in zip(combined_stats.keys(), combined_stats_sums)}
+
+ # Total statistics.
+ combined_stats['total/epochs'] = epoch + 1
+ combined_stats['total/steps'] = t
+
+ for key in sorted(combined_stats.keys()):
+ logger.record_tabular(key, combined_stats[key])
+
+ if rank == 0:
+ logger.dump_tabular()
+ logger.info('')
+ logdir = logger.get_dir()
+ if rank == 0 and logdir:
+ if hasattr(env, 'get_state'):
+ with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as f:
+ pickle.dump(env.get_state(), f)
+ if eval_env and hasattr(eval_env, 'get_state'):
+ with open(os.path.join(logdir, 'eval_env_state.pkl'), 'wb') as f:
+ pickle.dump(eval_env.get_state(), f)
+
+
+ return agent
diff --git a/baselines/baselines/ddpg/ddpg_learner.py b/baselines/baselines/ddpg/ddpg_learner.py
new file mode 100644
index 0000000000000000000000000000000000000000..44b2d5951b62dc25ce4143b3fa5dd5484c7fcd9b
--- /dev/null
+++ b/baselines/baselines/ddpg/ddpg_learner.py
@@ -0,0 +1,356 @@
+from functools import reduce
+
+import numpy as np
+import tensorflow as tf
+
+from baselines import logger
+from baselines.ddpg.models import Actor, Critic
+from baselines.common.mpi_running_mean_std import RunningMeanStd
+try:
+ from mpi4py import MPI
+ from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
+ from baselines.common.mpi_util import sync_from_root
+except ImportError:
+ MPI = None
+
+def normalize(x, stats):
+ if stats is None:
+ return x
+ return (x - stats.mean) / (stats.std + 1e-8)
+
+
+def denormalize(x, stats):
+ if stats is None:
+ return x
+ return x * stats.std + stats.mean
+
+@tf.function
+def reduce_std(x, axis=None, keepdims=False):
+ return tf.sqrt(reduce_var(x, axis=axis, keepdims=keepdims))
+
+def reduce_var(x, axis=None, keepdims=False):
+ m = tf.reduce_mean(x, axis=axis, keepdims=True)
+ devs_squared = tf.square(x - m)
+ return tf.reduce_mean(devs_squared, axis=axis, keepdims=keepdims)
+
+@tf.function
+def update_perturbed_actor(actor, perturbed_actor, param_noise_stddev):
+
+ for var, perturbed_var in zip(actor.variables, perturbed_actor.variables):
+ if var in actor.perturbable_vars:
+ perturbed_var.assign(var + tf.random.normal(shape=tf.shape(var), mean=0., stddev=param_noise_stddev))
+ else:
+ perturbed_var.assign(var)
+
+
+class DDPG(tf.Module):
+ def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
+ gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True,
+ batch_size=128, observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
+ critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.):
+
+ # Parameters.
+ self.gamma = gamma
+ self.tau = tau
+ self.memory = memory
+ self.normalize_observations = normalize_observations
+ self.normalize_returns = normalize_returns
+ self.action_noise = action_noise
+ self.param_noise = param_noise
+ self.action_range = action_range
+ self.return_range = return_range
+ self.observation_range = observation_range
+ self.observation_shape = observation_shape
+ self.critic = critic
+ self.actor = actor
+ self.clip_norm = clip_norm
+ self.enable_popart = enable_popart
+ self.reward_scale = reward_scale
+ self.batch_size = batch_size
+ self.stats_sample = None
+ self.critic_l2_reg = critic_l2_reg
+ self.actor_lr = tf.constant(actor_lr)
+ self.critic_lr = tf.constant(critic_lr)
+
+ # Observation normalization.
+ if self.normalize_observations:
+ with tf.name_scope('obs_rms'):
+ self.obs_rms = RunningMeanStd(shape=observation_shape)
+ else:
+ self.obs_rms = None
+
+ # Return normalization.
+ if self.normalize_returns:
+ with tf.name_scope('ret_rms'):
+ self.ret_rms = RunningMeanStd()
+ else:
+ self.ret_rms = None
+
+ # Create target networks.
+ self.target_critic = Critic(actor.nb_actions, observation_shape, name='target_critic', network=critic.network, **critic.network_kwargs)
+ self.target_actor = Actor(actor.nb_actions, observation_shape, name='target_actor', network=actor.network, **actor.network_kwargs)
+
+ # Set up parts.
+ if self.param_noise is not None:
+ self.setup_param_noise()
+
+ if MPI is not None:
+ comm = MPI.COMM_WORLD
+ self.actor_optimizer = MpiAdamOptimizer(comm, self.actor.trainable_variables)
+ self.critic_optimizer = MpiAdamOptimizer(comm, self.critic.trainable_variables)
+ else:
+ self.actor_optimizer = tf.keras.optimizers.Adam(learning_rate=actor_lr)
+ self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=critic_lr)
+
+ logger.info('setting up actor optimizer')
+ actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_variables]
+ actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
+ logger.info(' actor shapes: {}'.format(actor_shapes))
+ logger.info(' actor params: {}'.format(actor_nb_params))
+ logger.info('setting up critic optimizer')
+ critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_variables]
+ critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
+ logger.info(' critic shapes: {}'.format(critic_shapes))
+ logger.info(' critic params: {}'.format(critic_nb_params))
+ if self.critic_l2_reg > 0.:
+ critic_reg_vars = []
+ for layer in self.critic.network_builder.layers[1:]:
+ critic_reg_vars.append(layer.kernel)
+ for var in critic_reg_vars:
+ logger.info(' regularizing: {}'.format(var.name))
+ logger.info(' applying l2 regularization with {}'.format(self.critic_l2_reg))
+
+ logger.info('setting up critic target updates ...')
+ for var, target_var in zip(self.critic.variables, self.target_critic.variables):
+ logger.info(' {} <- {}'.format(target_var.name, var.name))
+ logger.info('setting up actor target updates ...')
+ for var, target_var in zip(self.actor.variables, self.target_actor.variables):
+ logger.info(' {} <- {}'.format(target_var.name, var.name))
+
+ if self.param_noise:
+ logger.info('setting up param noise')
+ for var, perturbed_var in zip(self.actor.variables, self.perturbed_actor.variables):
+ if var in actor.perturbable_vars:
+ logger.info(' {} <- {} + noise'.format(perturbed_var.name, var.name))
+ else:
+ logger.info(' {} <- {}'.format(perturbed_var.name, var.name))
+ for var, perturbed_var in zip(self.actor.variables, self.perturbed_adaptive_actor.variables):
+ if var in actor.perturbable_vars:
+ logger.info(' {} <- {} + noise'.format(perturbed_var.name, var.name))
+ else:
+ logger.info(' {} <- {}'.format(perturbed_var.name, var.name))
+
+ if self.normalize_returns and self.enable_popart:
+ self.setup_popart()
+
+ self.initial_state = None # recurrent architectures not supported yet
+
+
+ def setup_param_noise(self):
+ assert self.param_noise is not None
+
+ # Configure perturbed actor.
+ self.perturbed_actor = Actor(self.actor.nb_actions, self.observation_shape, name='param_noise_actor', network=self.actor.network, **self.actor.network_kwargs)
+
+ # Configure separate copy for stddev adoption.
+ self.perturbed_adaptive_actor = Actor(self.actor.nb_actions, self.observation_shape, name='adaptive_param_noise_actor', network=self.actor.network, **self.actor.network_kwargs)
+
+ def setup_popart(self):
+ # See https://arxiv.org/pdf/1602.07714.pdf for details.
+ for vs in [self.critic.output_vars, self.target_critic.output_vars]:
+ assert len(vs) == 2
+ M, b = vs
+ assert 'kernel' in M.name
+ assert 'bias' in b.name
+ assert M.get_shape()[-1] == 1
+ assert b.get_shape()[-1] == 1
+
+ @tf.function
+ def step(self, obs, apply_noise=True, compute_Q=True):
+ normalized_obs = tf.clip_by_value(normalize(obs, self.obs_rms), self.observation_range[0], self.observation_range[1])
+ actor_tf = self.actor(normalized_obs)
+ if self.param_noise is not None and apply_noise:
+ action = self.perturbed_actor(normalized_obs)
+ else:
+ action = actor_tf
+
+ if compute_Q:
+ normalized_critic_with_actor_tf = self.critic(normalized_obs, actor_tf)
+ q = denormalize(tf.clip_by_value(normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
+ else:
+ q = None
+
+ if self.action_noise is not None and apply_noise:
+ noise = self.action_noise()
+ action += noise
+ action = tf.clip_by_value(action, self.action_range[0], self.action_range[1])
+
+ return action, q, None, None
+
+ def store_transition(self, obs0, action, reward, obs1, terminal1):
+ reward *= self.reward_scale
+
+ B = obs0.shape[0]
+ for b in range(B):
+ self.memory.append(obs0[b], action[b], reward[b], obs1[b], terminal1[b])
+ if self.normalize_observations:
+ self.obs_rms.update(np.array([obs0[b]]))
+
+ def train(self):
+ batch = self.memory.sample(batch_size=self.batch_size)
+ obs0, obs1 = tf.constant(batch['obs0']), tf.constant(batch['obs1'])
+ actions, rewards, terminals1 = tf.constant(batch['actions']), tf.constant(batch['rewards']), tf.constant(batch['terminals1'], dtype=tf.float32)
+ normalized_obs0, target_Q = self.compute_normalized_obs0_and_target_Q(obs0, obs1, rewards, terminals1)
+
+ if self.normalize_returns and self.enable_popart:
+ old_mean = self.ret_rms.mean
+ old_std = self.ret_rms.std
+ self.ret_rms.update(target_Q.flatten())
+ # renormalize Q outputs
+ new_mean = self.ret_rms.mean
+ new_std = self.ret_rms.std
+ for vs in [self.critic.output_vars, self.target_critic.output_vars]:
+ kernel, bias = vs
+ kernel.assign(kernel * old_std / new_std)
+ bias.assign((bias * old_std + old_mean - new_mean) / new_std)
+
+
+ actor_grads, actor_loss = self.get_actor_grads(normalized_obs0)
+ critic_grads, critic_loss = self.get_critic_grads(normalized_obs0, actions, target_Q)
+
+ if MPI is not None:
+ self.actor_optimizer.apply_gradients(actor_grads, self.actor_lr)
+ self.critic_optimizer.apply_gradients(critic_grads, self.critic_lr)
+ else:
+ self.actor_optimizer.apply_gradients(zip(actor_grads, self.actor.trainable_variables))
+ self.critic_optimizer.apply_gradients(zip(critic_grads, self.critic.trainable_variables))
+
+ return critic_loss, actor_loss
+
+ @tf.function
+ def compute_normalized_obs0_and_target_Q(self, obs0, obs1, rewards, terminals1):
+ normalized_obs0 = tf.clip_by_value(normalize(obs0, self.obs_rms), self.observation_range[0], self.observation_range[1])
+ normalized_obs1 = tf.clip_by_value(normalize(obs1, self.obs_rms), self.observation_range[0], self.observation_range[1])
+ Q_obs1 = denormalize(self.target_critic(normalized_obs1, self.target_actor(normalized_obs1)), self.ret_rms)
+ target_Q = rewards + (1. - terminals1) * self.gamma * Q_obs1
+ return normalized_obs0, target_Q
+
+ @tf.function
+ def get_actor_grads(self, normalized_obs0):
+ with tf.GradientTape() as tape:
+ actor_tf = self.actor(normalized_obs0)
+ normalized_critic_with_actor_tf = self.critic(normalized_obs0, actor_tf)
+ critic_with_actor_tf = denormalize(tf.clip_by_value(normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
+ actor_loss = -tf.reduce_mean(critic_with_actor_tf)
+ actor_grads = tape.gradient(actor_loss, self.actor.trainable_variables)
+ if self.clip_norm:
+ actor_grads = [tf.clip_by_norm(grad, clip_norm=self.clip_norm) for grad in actor_grads]
+ if MPI is not None:
+ actor_grads = tf.concat([tf.reshape(g, (-1,)) for g in actor_grads], axis=0)
+ return actor_grads, actor_loss
+
+ @tf.function
+ def get_critic_grads(self, normalized_obs0, actions, target_Q):
+ with tf.GradientTape() as tape:
+ normalized_critic_tf = self.critic(normalized_obs0, actions)
+ normalized_critic_target_tf = tf.clip_by_value(normalize(target_Q, self.ret_rms), self.return_range[0], self.return_range[1])
+ critic_loss = tf.reduce_mean(tf.square(normalized_critic_tf - normalized_critic_target_tf))
+ # The first is input layer, which is ignored here.
+ if self.critic_l2_reg > 0.:
+ # Ignore the first input layer.
+ for layer in self.critic.network_builder.layers[1:]:
+ # The original l2_regularizer takes half of sum square.
+ critic_loss += (self.critic_l2_reg / 2.)* tf.reduce_sum(tf.square(layer.kernel))
+ critic_grads = tape.gradient(critic_loss, self.critic.trainable_variables)
+ if self.clip_norm:
+ critic_grads = [tf.clip_by_norm(grad, clip_norm=self.clip_norm) for grad in critic_grads]
+ if MPI is not None:
+ critic_grads = tf.concat([tf.reshape(g, (-1,)) for g in critic_grads], axis=0)
+ return critic_grads, critic_loss
+
+
+ def initialize(self):
+ if MPI is not None:
+ sync_from_root(self.actor.trainable_variables + self.critic.trainable_variables)
+ self.target_actor.set_weights(self.actor.get_weights())
+ self.target_critic.set_weights(self.critic.get_weights())
+
+ @tf.function
+ def update_target_net(self):
+ for var, target_var in zip(self.actor.variables, self.target_actor.variables):
+ target_var.assign((1. - self.tau) * target_var + self.tau * var)
+ for var, target_var in zip(self.critic.variables, self.target_critic.variables):
+ target_var.assign((1. - self.tau) * target_var + self.tau * var)
+
+ def get_stats(self):
+
+ if self.stats_sample is None:
+ # Get a sample and keep that fixed for all further computations.
+ # This allows us to estimate the change in value for the same set of inputs.
+ self.stats_sample = self.memory.sample(batch_size=self.batch_size)
+ obs0 = self.stats_sample['obs0']
+ actions = self.stats_sample['actions']
+ normalized_obs0 = tf.clip_by_value(normalize(obs0, self.obs_rms), self.observation_range[0], self.observation_range[1])
+ normalized_critic_tf = self.critic(normalized_obs0, actions)
+ critic_tf = denormalize(tf.clip_by_value(normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
+ actor_tf = self.actor(normalized_obs0)
+ normalized_critic_with_actor_tf = self.critic(normalized_obs0, actor_tf)
+ critic_with_actor_tf = denormalize(tf.clip_by_value(normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
+
+ stats = {}
+ if self.normalize_returns:
+ stats['ret_rms_mean'] = self.ret_rms.mean
+ stats['ret_rms_std'] = self.ret_rms.std
+ if self.normalize_observations:
+ stats['obs_rms_mean'] = tf.reduce_mean(self.obs_rms.mean)
+ stats['obs_rms_std'] = tf.reduce_mean(self.obs_rms.std)
+ stats['reference_Q_mean'] = tf.reduce_mean(critic_tf)
+ stats['reference_Q_std'] = reduce_std(critic_tf)
+ stats['reference_actor_Q_mean'] = tf.reduce_mean(critic_with_actor_tf)
+ stats['reference_actor_Q_std'] = reduce_std(critic_with_actor_tf)
+ stats['reference_action_mean'] = tf.reduce_mean(actor_tf)
+ stats['reference_action_std'] = reduce_std(actor_tf)
+
+ if self.param_noise:
+ perturbed_actor_tf = self.perturbed_actor(normalized_obs0)
+ stats['reference_perturbed_action_mean'] = tf.reduce_mean(perturbed_actor_tf)
+ stats['reference_perturbed_action_std'] = reduce_std(perturbed_actor_tf)
+ stats.update(self.param_noise.get_stats())
+ return stats
+
+
+
+ def adapt_param_noise(self, obs0):
+ try:
+ from mpi4py import MPI
+ except ImportError:
+ MPI = None
+
+ if self.param_noise is None:
+ return 0.
+
+ mean_distance = self.get_mean_distance(obs0).numpy()
+
+ if MPI is not None:
+ mean_distance = MPI.COMM_WORLD.allreduce(mean_distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
+
+ self.param_noise.adapt(mean_distance)
+ return mean_distance
+
+ @tf.function
+ def get_mean_distance(self, obs0):
+ # Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
+ update_perturbed_actor(self.actor, self.perturbed_adaptive_actor, self.param_noise.current_stddev)
+
+ normalized_obs0 = tf.clip_by_value(normalize(obs0, self.obs_rms), self.observation_range[0], self.observation_range[1])
+ actor_tf = self.actor(normalized_obs0)
+ adaptive_actor_tf = self.perturbed_adaptive_actor(normalized_obs0)
+ mean_distance = tf.sqrt(tf.reduce_mean(tf.square(actor_tf - adaptive_actor_tf)))
+ return mean_distance
+
+ def reset(self):
+ # Reset internal state after an episode is complete.
+ if self.action_noise is not None:
+ self.action_noise.reset()
+ if self.param_noise is not None:
+ update_perturbed_actor(self.actor, self.perturbed_actor, self.param_noise.current_stddev)
diff --git a/baselines/baselines/ddpg/memory.py b/baselines/baselines/ddpg/memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..715f4c08f0faccf7c4f7287c554a0ca715cca286
--- /dev/null
+++ b/baselines/baselines/ddpg/memory.py
@@ -0,0 +1,83 @@
+import numpy as np
+
+
+class RingBuffer(object):
+ def __init__(self, maxlen, shape, dtype='float32'):
+ self.maxlen = maxlen
+ self.start = 0
+ self.length = 0
+ self.data = np.zeros((maxlen,) + shape).astype(dtype)
+
+ def __len__(self):
+ return self.length
+
+ def __getitem__(self, idx):
+ if idx < 0 or idx >= self.length:
+ raise KeyError()
+ return self.data[(self.start + idx) % self.maxlen]
+
+ def get_batch(self, idxs):
+ return self.data[(self.start + idxs) % self.maxlen]
+
+ def append(self, v):
+ if self.length < self.maxlen:
+ # We have space, simply increase the length.
+ self.length += 1
+ elif self.length == self.maxlen:
+ # No space, "remove" the first item.
+ self.start = (self.start + 1) % self.maxlen
+ else:
+ # This should never happen.
+ raise RuntimeError()
+ self.data[(self.start + self.length - 1) % self.maxlen] = v
+
+
+def array_min2d(x):
+ x = np.array(x)
+ if x.ndim >= 2:
+ return x
+ return x.reshape(-1, 1)
+
+
+class Memory(object):
+ def __init__(self, limit, action_shape, observation_shape):
+ self.limit = limit
+
+ self.observations0 = RingBuffer(limit, shape=observation_shape)
+ self.actions = RingBuffer(limit, shape=action_shape)
+ self.rewards = RingBuffer(limit, shape=(1,))
+ self.terminals1 = RingBuffer(limit, shape=(1,))
+ self.observations1 = RingBuffer(limit, shape=observation_shape)
+
+ def sample(self, batch_size):
+ # Draw such that we always have a proceeding element.
+ batch_idxs = np.random.randint(self.nb_entries - 2, size=batch_size)
+
+ obs0_batch = self.observations0.get_batch(batch_idxs)
+ obs1_batch = self.observations1.get_batch(batch_idxs)
+ action_batch = self.actions.get_batch(batch_idxs)
+ reward_batch = self.rewards.get_batch(batch_idxs)
+ terminal1_batch = self.terminals1.get_batch(batch_idxs)
+
+ result = {
+ 'obs0': array_min2d(obs0_batch),
+ 'obs1': array_min2d(obs1_batch),
+ 'rewards': array_min2d(reward_batch),
+ 'actions': array_min2d(action_batch),
+ 'terminals1': array_min2d(terminal1_batch),
+ }
+ return result
+
+ def append(self, obs0, action, reward, obs1, terminal1, training=True):
+ if not training:
+ return
+
+ self.observations0.append(obs0)
+ self.actions.append(action)
+ self.rewards.append(reward)
+ self.observations1.append(obs1)
+ self.terminals1.append(terminal1)
+
+ @property
+ def nb_entries(self):
+ return len(self.observations0)
diff --git a/baselines/baselines/ddpg/models.py b/baselines/baselines/ddpg/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..175f9b862db341bd9f1b2c92b131da4f9df3383d
--- /dev/null
+++ b/baselines/baselines/ddpg/models.py
@@ -0,0 +1,49 @@
+import tensorflow as tf
+from baselines.common.models import get_network_builder
+
+
+class Model(tf.keras.Model):
+ def __init__(self, name, network='mlp', **network_kwargs):
+ super(Model, self).__init__(name=name)
+ self.network = network
+ self.network_kwargs = network_kwargs
+
+ @property
+ def perturbable_vars(self):
+ return [var for var in self.trainable_variables if 'layer_normalization' not in var.name]
+
+
+class Actor(Model):
+ def __init__(self, nb_actions, ob_shape, name='actor', network='mlp', **network_kwargs):
+ super().__init__(name=name, network=network, **network_kwargs)
+ self.nb_actions = nb_actions
+ self.network_builder = get_network_builder(network)(**network_kwargs)(ob_shape)
+ self.output_layer = tf.keras.layers.Dense(units=self.nb_actions,
+ activation=tf.keras.activations.tanh,
+ kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3))
+ _ = self.output_layer(self.network_builder.outputs[0])
+
+ @tf.function
+ def call(self, obs):
+ return self.output_layer(self.network_builder(obs))
+
+
+class Critic(Model):
+ def __init__(self, nb_actions, ob_shape, name='critic', network='mlp', **network_kwargs):
+ super().__init__(name=name, network=network, **network_kwargs)
+ self.layer_norm = True
+ self.network_builder = get_network_builder(network)(**network_kwargs)((ob_shape[0] + nb_actions,))
+ self.output_layer = tf.keras.layers.Dense(units=1,
+ kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3),
+ name='output')
+ _ = self.output_layer(self.network_builder.outputs[0])
+
+ @tf.function
+ def call(self, obs, actions):
+ x = tf.concat([obs, actions], axis=-1) # this assumes observation and action can be concatenated
+ x = self.network_builder(x)
+ return self.output_layer(x)
+
+ @property
+ def output_vars(self):
+ return self.output_layer.trainable_variables
diff --git a/baselines/baselines/ddpg/noise.py b/baselines/baselines/ddpg/noise.py
new file mode 100644
index 0000000000000000000000000000000000000000..c48d0d6a2288677e05950623f5c93d1e87c2071a
--- /dev/null
+++ b/baselines/baselines/ddpg/noise.py
@@ -0,0 +1,67 @@
+import numpy as np
+
+
+class AdaptiveParamNoiseSpec(object):
+ def __init__(self, initial_stddev=0.1, desired_action_stddev=0.1, adoption_coefficient=1.01):
+ self.initial_stddev = initial_stddev
+ self.desired_action_stddev = desired_action_stddev
+ self.adoption_coefficient = adoption_coefficient
+
+ self.current_stddev = initial_stddev
+
+ def adapt(self, distance):
+ if distance > self.desired_action_stddev:
+ # Decrease stddev.
+ self.current_stddev /= self.adoption_coefficient
+ else:
+ # Increase stddev.
+ self.current_stddev *= self.adoption_coefficient
+
+ def get_stats(self):
+ stats = {
+ 'param_noise_stddev': self.current_stddev,
+ }
+ return stats
+
+ def __repr__(self):
+ fmt = 'AdaptiveParamNoiseSpec(initial_stddev={}, desired_action_stddev={}, adoption_coefficient={})'
+ return fmt.format(self.initial_stddev, self.desired_action_stddev, self.adoption_coefficient)
+
+
+class ActionNoise(object):
+ def reset(self):
+ pass
+
+
+class NormalActionNoise(ActionNoise):
+ def __init__(self, mu, sigma):
+ self.mu = mu
+ self.sigma = sigma
+
+ def __call__(self):
+ return np.random.normal(self.mu, self.sigma)
+
+ def __repr__(self):
+ return 'NormalActionNoise(mu={}, sigma={})'.format(self.mu, self.sigma)
+
+
+# Based on http://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab
+class OrnsteinUhlenbeckActionNoise(ActionNoise):
+ def __init__(self, mu, sigma, theta=.15, dt=1e-2, x0=None):
+ self.theta = theta
+ self.mu = mu
+ self.sigma = sigma
+ self.dt = dt
+ self.x0 = x0
+ self.reset()
+
+ def __call__(self):
+ x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + self.sigma * np.sqrt(self.dt) * np.random.normal(size=self.mu.shape)
+ self.x_prev = x
+ return x
+
+ def reset(self):
+ self.x_prev = self.x0 if self.x0 is not None else np.zeros_like(self.mu)
+
+ def __repr__(self):
+ return 'OrnsteinUhlenbeckActionNoise(mu={}, sigma={})'.format(self.mu, self.sigma)
diff --git a/baselines/baselines/deepq/README.md b/baselines/baselines/deepq/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..51fcbefb723a7e51a4b518550cb2795d74f5fa45
--- /dev/null
+++ b/baselines/baselines/deepq/README.md
@@ -0,0 +1,37 @@
+## If you are curious.
+
+##### Train a Cartpole agent and watch it play once it converges!
+
+Here's a list of commands to run to quickly get a working example:
+
+<img src="../../data/cartpole.gif" width="25%" />
+
+
+```bash
+# Train model and save the results to cartpole_model.pkl
+python -m baselines.run --alg=deepq --env=CartPole-v0 --save_path=./cartpole_model.pkl --num_timesteps=1e5
+# Load the model saved in cartpole_model.pkl and visualize the learned policy
+python -m baselines.run --alg=deepq --env=CartPole-v0 --load_path=./cartpole_model.pkl --num_timesteps=0 --play
+```
+
+## If you wish to apply DQN to solve a problem.
+
+Check out our simple agent trained with one stop shop `deepq.learn` function.
+
+- [baselines/deepq/experiments/train_cartpole.py](experiments/train_cartpole.py) - train a Cartpole agent.
+
+In particular notice that once `deepq.learn` finishes training it returns `act` function which can be used to select actions in the environment. Once trained you can easily save it and load at later time. Complimentary file `enjoy_cartpole.py` loads and visualizes the learned policy.
+
+## If you wish to experiment with the algorithm
+
+##### Check out the examples
+
+- [baselines/deepq/experiments/custom_cartpole.py](experiments/custom_cartpole.py) - Cartpole training with more fine grained control over the internals of DQN algorithm.
+- [baselines/deepq/defaults.py](defaults.py) - settings for training on atari. Run
+
+```bash
+python -m baselines.run --alg=deepq --env=PongNoFrameskip-v4
+```
+to train on Atari Pong (see more in repo-wide [README.md](../../README.md#training-models))
+
+
diff --git a/baselines/baselines/deepq/__init__.py b/baselines/baselines/deepq/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..79140c2bc02ea20d5ba81e72d366f70b0a8e4aff
--- /dev/null
+++ b/baselines/baselines/deepq/__init__.py
@@ -0,0 +1,8 @@
+from baselines.deepq import models # noqa F401
+from baselines.deepq.deepq_learner import DEEPQ # noqa F401
+from baselines.deepq.deepq import learn # noqa F401
+from baselines.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer # noqa F401
+
+def wrap_atari_dqn(env):
+ from baselines.common.atari_wrappers import wrap_deepmind
+ return wrap_deepmind(env, frame_stack=True, scale=False)
diff --git a/baselines/baselines/deepq/deepq.py b/baselines/baselines/deepq/deepq.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b537ba778f004988ab14670f7414dac3ec0e472
--- /dev/null
+++ b/baselines/baselines/deepq/deepq.py
@@ -0,0 +1,234 @@
+import os.path as osp
+
+import tensorflow as tf
+import numpy as np
+
+from baselines import logger
+from baselines.common.schedules import LinearSchedule
+from baselines.common.vec_env.vec_env import VecEnv
+from baselines.common import set_global_seeds
+
+from baselines import deepq
+from baselines.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer
+
+from baselines.deepq.models import build_q_func
+
+
+
+def learn(env,
+ network,
+ seed=None,
+ lr=5e-4,
+ total_timesteps=100000,
+ buffer_size=50000,
+ exploration_fraction=0.1,
+ exploration_final_eps=0.02,
+ train_freq=1,
+ batch_size=32,
+ print_freq=100,
+ checkpoint_freq=10000,
+ checkpoint_path=None,
+ learning_starts=1000,
+ gamma=1.0,
+ target_network_update_freq=500,
+ prioritized_replay=False,
+ prioritized_replay_alpha=0.6,
+ prioritized_replay_beta0=0.4,
+ prioritized_replay_beta_iters=None,
+ prioritized_replay_eps=1e-6,
+ param_noise=False,
+ callback=None,
+ load_path=None,
+ **network_kwargs
+ ):
+ """Train a deepq model.
+
+ Parameters
+ -------
+ env: gym.Env
+ environment to train on
+ network: string or a function
+ neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
+ (mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
+ will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
+ seed: int or None
+ prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
+ lr: float
+ learning rate for adam optimizer
+ total_timesteps: int
+ number of env steps to optimizer for
+ buffer_size: int
+ size of the replay buffer
+ exploration_fraction: float
+ fraction of entire training period over which the exploration rate is annealed
+ exploration_final_eps: float
+ final value of random action probability
+ train_freq: int
+ update the model every `train_freq` steps.
+ set to None to disable printing
+ batch_size: int
+ size of a batched sampled from replay buffer for training
+ print_freq: int
+ how often to print out training progress
+ set to None to disable printing
+ checkpoint_freq: int
+ how often to save the model. This is so that the best version is restored
+ at the end of the training. If you do not wish to restore the best version at
+ the end of the training set this variable to None.
+ learning_starts: int
+ how many steps of the model to collect transitions for before learning starts
+ gamma: float
+ discount factor
+ target_network_update_freq: int
+ update the target network every `target_network_update_freq` steps.
+ prioritized_replay: True
+ if True prioritized replay buffer will be used.
+ prioritized_replay_alpha: float
+ alpha parameter for prioritized replay buffer
+ prioritized_replay_beta0: float
+ initial value of beta for prioritized replay buffer
+ prioritized_replay_beta_iters: int
+ number of iterations over which beta will be annealed from initial value
+ to 1.0. If set to None equals to total_timesteps.
+ prioritized_replay_eps: float
+ epsilon to add to the TD errors when updating priorities.
+ param_noise: bool
+ whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
+ callback: (locals, globals) -> None
+ function called at every steps with state of the algorithm.
+ If callback returns true training stops.
+ load_path: str
+ path to load the model from. (default: None)
+ **network_kwargs
+ additional keyword arguments to pass to the network builder.
+
+ Returns
+ -------
+ act: ActWrapper
+ Wrapper over act function. Adds ability to save it and load it.
+ See header of baselines/deepq/categorical.py for details on the act function.
+ """
+ # Create all the functions necessary to train the model
+
+ set_global_seeds(seed)
+
+ q_func = build_q_func(network, **network_kwargs)
+
+ # capture the shape outside the closure so that the env object is not serialized
+ # by cloudpickle when serializing make_obs_ph
+
+ observation_space = env.observation_space
+
+ model = deepq.DEEPQ(
+ q_func=q_func,
+ observation_shape=env.observation_space.shape,
+ num_actions=env.action_space.n,
+ lr=lr,
+ grad_norm_clipping=10,
+ gamma=gamma,
+ param_noise=param_noise
+ )
+
+ if load_path is not None:
+ load_path = osp.expanduser(load_path)
+ ckpt = tf.train.Checkpoint(model=model)
+ manager = tf.train.CheckpointManager(ckpt, load_path, max_to_keep=None)
+ ckpt.restore(manager.latest_checkpoint)
+ print("Restoring from {}".format(manager.latest_checkpoint))
+
+ # Create the replay buffer
+ if prioritized_replay:
+ replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
+ if prioritized_replay_beta_iters is None:
+ prioritized_replay_beta_iters = total_timesteps
+ beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
+ initial_p=prioritized_replay_beta0,
+ final_p=1.0)
+ else:
+ replay_buffer = ReplayBuffer(buffer_size)
+ beta_schedule = None
+ # Create the schedule for exploration starting from 1.
+ exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),
+ initial_p=1.0,
+ final_p=exploration_final_eps)
+
+ model.update_target()
+
+ episode_rewards = [0.0]
+ saved_mean_reward = None
+ obs = env.reset()
+ # always mimic the vectorized env
+ if not isinstance(env, VecEnv):
+ obs = np.expand_dims(np.array(obs), axis=0)
+ reset = True
+
+ for t in range(total_timesteps):
+ if callback is not None:
+ if callback(locals(), globals()):
+ break
+ kwargs = {}
+ if not param_noise:
+ update_eps = tf.constant(exploration.value(t))
+ update_param_noise_threshold = 0.
+ else:
+ update_eps = tf.constant(0.)
+ # Compute the threshold such that the KL divergence between perturbed and non-perturbed
+ # policy is comparable to eps-greedy exploration with eps = exploration.value(t).
+ # See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
+ # for detailed explanation.
+ update_param_noise_threshold = -np.log(1. - exploration.value(t) + exploration.value(t) / float(env.action_space.n))
+ kwargs['reset'] = reset
+ kwargs['update_param_noise_threshold'] = update_param_noise_threshold
+ kwargs['update_param_noise_scale'] = True
+ action, _, _, _ = model.step(tf.constant(obs), update_eps=update_eps, **kwargs)
+ action = action[0].numpy()
+ reset = False
+ new_obs, rew, done, _ = env.step(action)
+ # Store transition in the replay buffer.
+ if not isinstance(env, VecEnv):
+ new_obs = np.expand_dims(np.array(new_obs), axis=0)
+ replay_buffer.add(obs[0], action, rew, new_obs[0], float(done))
+ else:
+ replay_buffer.add(obs[0], action, rew[0], new_obs[0], float(done[0]))
+ # # Store transition in the replay buffer.
+ # replay_buffer.add(obs, action, rew, new_obs, float(done))
+ obs = new_obs
+
+ episode_rewards[-1] += rew
+ if done:
+ obs = env.reset()
+ if not isinstance(env, VecEnv):
+ obs = np.expand_dims(np.array(obs), axis=0)
+ episode_rewards.append(0.0)
+ reset = True
+
+ if t > learning_starts and t % train_freq == 0:
+ # Minimize the error in Bellman's equation on a batch sampled from replay buffer.
+ if prioritized_replay:
+ experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t))
+ (obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
+ else:
+ obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(batch_size)
+ weights, batch_idxes = np.ones_like(rewards), None
+ obses_t, obses_tp1 = tf.constant(obses_t), tf.constant(obses_tp1)
+ actions, rewards, dones = tf.constant(actions), tf.constant(rewards), tf.constant(dones)
+ weights = tf.constant(weights)
+ td_errors = model.train(obses_t, actions, rewards, obses_tp1, dones, weights)
+ if prioritized_replay:
+ new_priorities = np.abs(td_errors) + prioritized_replay_eps
+ replay_buffer.update_priorities(batch_idxes, new_priorities)
+
+ if t > learning_starts and t % target_network_update_freq == 0:
+ # Update target network periodically.
+ model.update_target()
+
+ mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
+ num_episodes = len(episode_rewards)
+ if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
+ logger.record_tabular("steps", t)
+ logger.record_tabular("episodes", num_episodes)
+ logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
+ logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
+ logger.dump_tabular()
+
+ return model
diff --git a/baselines/baselines/deepq/deepq_learner.py b/baselines/baselines/deepq/deepq_learner.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b314a6293583606297b0cc46c6bc0169acec3bb
--- /dev/null
+++ b/baselines/baselines/deepq/deepq_learner.py
@@ -0,0 +1,191 @@
+"""Deep Q model
+
+The functions in this model:
+
+======= step ========
+
+ Function to chose an action given an observation
+
+ Parameters
+ ----------
+ observation: tensor
+ Observation that can be feed into the output of make_obs_ph
+ stochastic: bool
+ if set to False all the actions are always deterministic (default False)
+ update_eps: float
+ update epsilon a new value, if negative not update happens
+ (default: no update)
+
+ Returns
+ -------
+ Tensor of dtype tf.int64 and shape (BATCH_SIZE,) with an action to be performed for
+ every element of the batch.
+
+
+(NOT IMPLEMENTED YET)
+======= step (in case of parameter noise) ========
+
+ Function to chose an action given an observation
+
+ Parameters
+ ----------
+ observation: object
+ Observation that can be feed into the output of make_obs_ph
+ stochastic: bool
+ if set to False all the actions are always deterministic (default False)
+ update_eps: float
+ update epsilon to a new value, if negative no update happens
+ (default: no update)
+ reset: bool
+ reset the perturbed policy by sampling a new perturbation
+ update_param_noise_threshold: float
+ the desired threshold for the difference between non-perturbed and perturbed policy
+ update_param_noise_scale: bool
+ whether or not to update the scale of the noise for the next time it is re-perturbed
+
+ Returns
+ -------
+ Tensor of dtype tf.int64 and shape (BATCH_SIZE,) with an action to be performed for
+ every element of the batch.
+
+
+======= train =======
+
+ Function that takes a transition (s,a,r,s',d) and optimizes Bellman equation's error:
+
+ td_error = Q(s,a) - (r + gamma * (1-d) * max_a' Q(s', a'))
+ loss = huber_loss[td_error]
+
+ Parameters
+ ----------
+ obs_t: object
+ a batch of observations
+ action: np.array
+ actions that were selected upon seeing obs_t.
+ dtype must be int32 and shape must be (batch_size,)
+ reward: np.array
+ immediate reward attained after executing those actions
+ dtype must be float32 and shape must be (batch_size,)
+ obs_tp1: object
+ observations that followed obs_t
+ done: np.array
+ 1 if obs_t was the last observation in the episode and 0 otherwise
+ obs_tp1 gets ignored, but must be of the valid shape.
+ dtype must be float32 and shape must be (batch_size,)
+ weight: np.array
+ imporance weights for every element of the batch (gradient is multiplied
+ by the importance weight) dtype must be float32 and shape must be (batch_size,)
+
+ Returns
+ -------
+ td_error: np.array
+ a list of differences between Q(s,a) and the target in Bellman's equation.
+ dtype is float32 and shape is (batch_size,)
+
+======= update_target ========
+
+ copy the parameters from optimized Q function to the target Q function.
+ In Q learning we actually optimize the following error:
+
+ Q(s,a) - (r + gamma * max_a' Q'(s', a'))
+
+ Where Q' is lagging behind Q to stablize the learning. For example for Atari
+
+ Q' is set to Q once every 10000 updates training steps.
+
+"""
+import tensorflow as tf
+
+@tf.function
+def huber_loss(x, delta=1.0):
+ """Reference: https://en.wikipedia.org/wiki/Huber_loss"""
+ return tf.where(
+ tf.abs(x) < delta,
+ tf.square(x) * 0.5,
+ delta * (tf.abs(x) - 0.5 * delta)
+ )
+
+class DEEPQ(tf.Module):
+
+ def __init__(self, q_func, observation_shape, num_actions, lr, grad_norm_clipping=None, gamma=1.0,
+ double_q=True, param_noise=False, param_noise_filter_func=None):
+
+ self.num_actions = num_actions
+ self.gamma = gamma
+ self.double_q = double_q
+ self.param_noise = param_noise
+ self.param_noise_filter_func = param_noise_filter_func
+ self.grad_norm_clipping = grad_norm_clipping
+
+ self.optimizer = tf.keras.optimizers.Adam(lr)
+
+ with tf.name_scope('q_network'):
+ self.q_network = q_func(observation_shape, num_actions)
+ with tf.name_scope('target_q_network'):
+ self.target_q_network = q_func(observation_shape, num_actions)
+ self.eps = tf.Variable(0., name="eps")
+
+ @tf.function
+ def step(self, obs, stochastic=True, update_eps=-1):
+ if self.param_noise:
+ raise ValueError('not supporting noise yet')
+ else:
+ q_values = self.q_network(obs)
+ deterministic_actions = tf.argmax(q_values, axis=1)
+ batch_size = tf.shape(obs)[0]
+ random_actions = tf.random.uniform(tf.stack([batch_size]), minval=0, maxval=self.num_actions, dtype=tf.int64)
+ chose_random = tf.random.uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < self.eps
+ stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
+
+ if stochastic:
+ output_actions = stochastic_actions
+ else:
+ output_actions = deterministic_actions
+
+ if update_eps >= 0:
+ self.eps.assign(update_eps)
+
+ return output_actions, None, None, None
+
+ @tf.function()
+ def train(self, obs0, actions, rewards, obs1, dones, importance_weights):
+ with tf.GradientTape() as tape:
+ q_t = self.q_network(obs0)
+ q_t_selected = tf.reduce_sum(q_t * tf.one_hot(actions, self.num_actions, dtype=tf.float32), 1)
+
+ q_tp1 = self.target_q_network(obs1)
+
+ if self.double_q:
+ q_tp1_using_online_net = self.q_network(obs1)
+ q_tp1_best_using_online_net = tf.argmax(q_tp1_using_online_net, 1)
+ q_tp1_best = tf.reduce_sum(q_tp1 * tf.one_hot(q_tp1_best_using_online_net, self.num_actions, dtype=tf.float32), 1)
+ else:
+ q_tp1_best = tf.reduce_max(q_tp1, 1)
+
+ dones = tf.cast(dones, q_tp1_best.dtype)
+ q_tp1_best_masked = (1.0 - dones) * q_tp1_best
+
+ q_t_selected_target = rewards + self.gamma * q_tp1_best_masked
+
+ td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
+ errors = huber_loss(td_error)
+ weighted_error = tf.reduce_mean(importance_weights * errors)
+
+ grads = tape.gradient(weighted_error, self.q_network.trainable_variables)
+ if self.grad_norm_clipping:
+ clipped_grads = []
+ for grad in grads:
+ clipped_grads.append(tf.clip_by_norm(grad, self.grad_norm_clipping))
+ clipped_grads = grads
+ grads_and_vars = zip(grads, self.q_network.trainable_variables)
+ self.optimizer.apply_gradients(grads_and_vars)
+
+ return td_error
+
+ @tf.function(autograph=False)
+ def update_target(self):
+ q_vars = self.q_network.trainable_variables
+ target_q_vars = self.target_q_network.trainable_variables
+ for var, var_target in zip(q_vars, target_q_vars):
+ var_target.assign(var)
+
diff --git a/baselines/baselines/deepq/defaults.py b/baselines/baselines/deepq/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..d41fb180e8590ca5f72432c8f2ffac63ae99bfd9
--- /dev/null
+++ b/baselines/baselines/deepq/defaults.py
@@ -0,0 +1,21 @@
+def atari():
+ return dict(
+ network='conv_only',
+ lr=1e-4,
+ buffer_size=10000,
+ exploration_fraction=0.1,
+ exploration_final_eps=0.01,
+ train_freq=4,
+ learning_starts=10000,
+ target_network_update_freq=1000,
+ gamma=0.99,
+ prioritized_replay=True,
+ prioritized_replay_alpha=0.6,
+ checkpoint_freq=10000,
+ checkpoint_path=None,
+ dueling=True
+ )
+
+def retro():
+ return atari()
+
diff --git a/baselines/baselines/deepq/models.py b/baselines/baselines/deepq/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..70822a2ddb2fac3f19bf586bf46d4e2438a03307
--- /dev/null
+++ b/baselines/baselines/deepq/models.py
@@ -0,0 +1,47 @@
+import tensorflow as tf
+
+
+def build_q_func(network, hiddens=[256], dueling=True, layer_norm=False, **network_kwargs):
+ if isinstance(network, str):
+ from baselines.common.models import get_network_builder
+ network = get_network_builder(network)(**network_kwargs)
+
+ def q_func_builder(input_shape, num_actions):
+ # the sub Functional model which does not include the top layer.
+ model = network(input_shape)
+
+ # wrapping the sub Functional model with layers that compute action scores into another Functional model.
+ latent = model.outputs
+ if len(latent) > 1:
+ if latent[1] is not None:
+ raise NotImplementedError("DQN is not compatible with recurrent policies yet")
+ latent = latent[0]
+
+ latent = tf.keras.layers.Flatten()(latent)
+
+ with tf.name_scope("action_value"):
+ action_out = latent
+ for hidden in hiddens:
+ action_out = tf.keras.layers.Dense(units=hidden, activation=None)(action_out)
+ if layer_norm:
+ action_out = tf.keras.layers.LayerNormalization(center=True, scale=True)(action_out)
+ action_out = tf.nn.relu(action_out)
+ action_scores = tf.keras.layers.Dense(units=num_actions, activation=None)(action_out)
+
+ if dueling:
+ with tf.name_scope("state_value"):
+ state_out = latent
+ for hidden in hiddens:
+ state_out = tf.keras.layers.Dense(units=hidden, activation=None)(state_out)
+ if layer_norm:
+ state_out = tf.keras.layers.LayerNormalization(center=True, scale=True)(state_out)
+ state_out = tf.nn.relu(state_out)
+ state_score = tf.keras.layers.Dense(units=1, activation=None)(state_out)
+ action_scores_mean = tf.reduce_mean(action_scores, 1)
+ action_scores_centered = action_scores - tf.expand_dims(action_scores_mean, 1)
+ q_out = state_score + action_scores_centered
+ else:
+ q_out = action_scores
+ return tf.keras.Model(inputs=model.inputs, outputs=[q_out])
+
+ return q_func_builder
diff --git a/baselines/baselines/deepq/replay_buffer.py b/baselines/baselines/deepq/replay_buffer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f848d751d5f896583a0f4a7f2452480672c5199d
--- /dev/null
+++ b/baselines/baselines/deepq/replay_buffer.py
@@ -0,0 +1,194 @@
+import numpy as np
+import random
+
+from baselines.common.segment_tree import SumSegmentTree, MinSegmentTree
+
+
+class ReplayBuffer(object):
+ def __init__(self, size):
+ """Create Replay buffer.
+
+ Parameters
+ ----------
+ size: int
+ Max number of transitions to store in the buffer. When the buffer
+ overflows the old memories are dropped.
+ """
+ self._storage = []
+ self._maxsize = size
+ self._next_idx = 0
+
+ def __len__(self):
+ return len(self._storage)
+
+ def add(self, obs_t, action, reward, obs_tp1, done):
+ data = (obs_t, action, reward, obs_tp1, done)
+
+ if self._next_idx >= len(self._storage):
+ self._storage.append(data)
+ else:
+ self._storage[self._next_idx] = data
+ self._next_idx = (self._next_idx + 1) % self._maxsize
+
+ def _encode_sample(self, idxes):
+ obses_t, actions, rewards, obses_tp1, dones = [], [], [], [], []
+ data = self._storage[0]
+ ob_dtype = data[0].dtype
+ ac_dtype = data[1].dtype
+ for i in idxes:
+ data = self._storage[i]
+ obs_t, action, reward, obs_tp1, done = data
+ obses_t.append(np.array(obs_t, copy=False))
+ actions.append(np.array(action, copy=False))
+ rewards.append(reward)
+ obses_tp1.append(np.array(obs_tp1, copy=False))
+ dones.append(done)
+ return np.array(obses_t, dtype=ob_dtype), np.array(actions, dtype=ac_dtype), np.array(rewards, dtype=np.float32), np.array(obses_tp1, dtype=ob_dtype), np.array(dones, dtype=np.float32)
+
+ def sample(self, batch_size):
+ """Sample a batch of experiences.
+
+ Parameters
+ ----------
+ batch_size: int
+ How many transitions to sample.
+
+ Returns
+ -------
+ obs_batch: np.array
+ batch of observations
+ act_batch: np.array
+ batch of actions executed given obs_batch
+ rew_batch: np.array
+ rewards received as results of executing act_batch
+ next_obs_batch: np.array
+ next set of observations seen after executing act_batch
+ done_mask: np.array
+ done_mask[i] = 1 if executing act_batch[i] resulted in
+ the end of an episode and 0 otherwise.
+ """
+ idxes = [random.randint(0, len(self._storage) - 1) for _ in range(batch_size)]
+ return self._encode_sample(idxes)
+
+
+class PrioritizedReplayBuffer(ReplayBuffer):
+ def __init__(self, size, alpha):
+ """Create Prioritized Replay buffer.
+
+ Parameters
+ ----------
+ size: int
+ Max number of transitions to store in the buffer. When the buffer
+ overflows the old memories are dropped.
+ alpha: float
+ how much prioritization is used
+ (0 - no prioritization, 1 - full prioritization)
+
+ See Also
+ --------
+ ReplayBuffer.__init__
+ """
+ super(PrioritizedReplayBuffer, self).__init__(size)
+ assert alpha >= 0
+ self._alpha = alpha
+
+ it_capacity = 1
+ while it_capacity < size:
+ it_capacity *= 2
+
+ self._it_sum = SumSegmentTree(it_capacity)
+ self._it_min = MinSegmentTree(it_capacity)
+ self._max_priority = 1.0
+
+ def add(self, *args, **kwargs):
+ """See ReplayBuffer.store_effect"""
+ idx = self._next_idx
+ super().add(*args, **kwargs)
+ self._it_sum[idx] = self._max_priority ** self._alpha
+ self._it_min[idx] = self._max_priority ** self._alpha
+
+ def _sample_proportional(self, batch_size):
+ res = []
+ p_total = self._it_sum.sum(0, len(self._storage) - 1)
+ every_range_len = p_total / batch_size
+ for i in range(batch_size):
+ mass = random.random() * every_range_len + i * every_range_len
+ idx = self._it_sum.find_prefixsum_idx(mass)
+ res.append(idx)
+ return res
+
+ def sample(self, batch_size, beta):
+ """Sample a batch of experiences.
+
+ compared to ReplayBuffer.sample
+ it also returns importance weights and idxes
+ of sampled experiences.
+
+
+ Parameters
+ ----------
+ batch_size: int
+ How many transitions to sample.
+ beta: float
+ To what degree to use importance weights
+ (0 - no corrections, 1 - full correction)
+
+ Returns
+ -------
+ obs_batch: np.array
+ batch of observations
+ act_batch: np.array
+ batch of actions executed given obs_batch
+ rew_batch: np.array
+ rewards received as results of executing act_batch
+ next_obs_batch: np.array
+ next set of observations seen after executing act_batch
+ done_mask: np.array
+ done_mask[i] = 1 if executing act_batch[i] resulted in
+ the end of an episode and 0 otherwise.
+ weights: np.array
+ Array of shape (batch_size,) and dtype np.float32
+ denoting importance weight of each sampled transition
+ idxes: np.array
+ Array of shape (batch_size,) and dtype np.int32
+ idexes in buffer of sampled experiences
+ """
+ assert beta > 0
+
+ idxes = self._sample_proportional(batch_size)
+
+ weights = []
+ p_min = self._it_min.min() / self._it_sum.sum()
+ max_weight = (p_min * len(self._storage)) ** (-beta)
+
+ for idx in idxes:
+ p_sample = self._it_sum[idx] / self._it_sum.sum()
+ weight = (p_sample * len(self._storage)) ** (-beta)
+ weights.append(weight / max_weight)
+ weights = np.array(weights, dtype=np.float32)
+ encoded_sample = self._encode_sample(idxes)
+ return tuple(list(encoded_sample) + [weights, idxes])
+
+ def update_priorities(self, idxes, priorities):
+ """Update priorities of sampled transitions.
+
+ sets priority of transition at index idxes[i] in buffer
+ to priorities[i].
+
+ Parameters
+ ----------
+ idxes: [int]
+ List of idxes of sampled transitions
+ priorities: [float]
+ List of updated priorities corresponding to
+ transitions at the sampled idxes denoted by
+ variable `idxes`.
+ """
+ assert len(idxes) == len(priorities)
+ for idx, priority in zip(idxes, priorities):
+ assert priority > 0
+ assert 0 <= idx < len(self._storage)
+ self._it_sum[idx] = priority ** self._alpha
+ self._it_min[idx] = priority ** self._alpha
+
+ self._max_priority = max(self._max_priority, priority)
diff --git a/baselines/baselines/her/README.md b/baselines/baselines/her/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..232b5053bda1c038c3e8f1fb3f897d2d3d68833b
--- /dev/null
+++ b/baselines/baselines/her/README.md
@@ -0,0 +1,85 @@
+# Hindsight Experience Replay
+For details on Hindsight Experience Replay (HER), please read the [paper](https://arxiv.org/abs/1707.01495).
+
+## How to use Hindsight Experience Replay
+
+### Getting started
+Training an agent is very simple:
+```bash
+python -m baselines.run --alg=her --env=FetchReach-v1 --num_timesteps=5000
+```
+This will train a DDPG+HER agent on the `FetchReach` environment.
+You should see the success rate go up quickly to `1.0`, which means that the agent achieves the
+desired goal in 100% of the cases (note how HER can solve it in <5k steps - try doing that with PPO by replacing her with ppo2 :))
+The training script logs other diagnostics as well. Policy at the end of the training can be saved using `--save_path` flag, for instance:
+```bash
+python -m baselines.run --alg=her --env=FetchReach-v1 --num_timesteps=5000 --save_path=~/policies/her/fetchreach5k
+```
+
+To inspect what the agent has learned, use the `--play` flag:
+```bash
+python -m baselines.run --alg=her --env=FetchReach-v1 --num_timesteps=5000 --play
+```
+(note `--play` can be combined with `--load_path`, which lets one load trained policies, for more results see [README.md](../../README.md))
+
+
+### Reproducing results
+In [Plappert et al. (2018)](https://arxiv.org/abs/1802.09464), 38 trajectories were generated in parallel
+(19 MPI processes, each generating computing gradients from 2 trajectories and aggregating).
+To reproduce that behaviour, use
+```bash
+mpirun -np 19 python -m baselines.run --num_env=2 --alg=her ...
+```
+This will require a machine with sufficient amount of physical CPU cores. In our experiments,
+we used [Azure's D15v2 instances](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/sizes),
+which have 20 physical cores. We only scheduled the experiment on 19 of those to leave some head-room on the system.
+
+
+## Hindsight Experience Replay with Demonstrations
+Using pre-recorded demonstrations to Overcome the exploration problem in HER based Reinforcement learning.
+For details, please read the [paper](https://arxiv.org/pdf/1709.10089.pdf).
+
+### Getting started
+The first step is to generate the demonstration dataset. This can be done in two ways, either by using a VR system to manipulate the arm using physical VR trackers or the simpler way is to write a script to carry out the respective task. Now some tasks can be complex and thus it would be difficult to write a hardcoded script for that task (eg. Fetch Push), but here our focus is on providing an algorithm that helps the agent to learn from demonstrations, and not on the demonstration generation paradigm itself. Thus the data collection part is left to the reader's choice.
+
+We provide a script for the Fetch Pick and Place task, to generate demonstrations for the Pick and Place task execute:
+```bash
+python experiment/data_generation/fetch_data_generation.py
+```
+This outputs ```data_fetch_random_100.npz``` file which is our data file.
+
+To launch training with demonstrations (more technically, with behaviour cloning loss as an auxilliary loss), run the following
+```bash
+python -m baselines.run --alg=her --env=FetchPickAndPlace-v1 --num_timesteps=2.5e6 --demo_file=/Path/to/demo_file.npz
+```
+This will train a DDPG+HER agent on the `FetchPickAndPlace` environment by using previously generated demonstration data.
+To inspect what the agent has learned, use the `--play` flag as described above.
+
+#### Configuration
+The provided configuration is for training an agent with HER without demonstrations, we need to change a few paramters for the HER algorithm to learn through demonstrations, to do that, set:
+
+* bc_loss: 1 - whether or not to use the behavior cloning loss as an auxilliary loss
+* q_filter: 1 - whether or not a Q value filter should be used on the Actor outputs
+* num_demo: 100 - number of expert demo episodes
+* demo_batch_size: 128 - number of samples to be used from the demonstrations buffer, per mpi thread
+* prm_loss_weight: 0.001 - Weight corresponding to the primary loss
+* aux_loss_weight: 0.0078 - Weight corresponding to the auxilliary loss also called the cloning loss
+
+Apart from these changes the reported results also have the following configurational changes:
+
+* n_cycles: 20 - per epoch
+* batch_size: 1024 - per mpi thread, total batch size
+* random_eps: 0.1 - percentage of time a random action is taken
+* noise_eps: 0.1 - std of gaussian noise added to not-completely-random actions
+
+These parameters can be changed either in [experiment/config.py](experiment/config.py) or passed to the command line as `--param=value`)
+
+### Results
+Training with demonstrations helps overcome the exploration problem and achieves a faster and better convergence. The following graphs contrast the difference between training with and without demonstration data, We report the mean Q values vs Epoch and the Success Rate vs Epoch:
+
+
+<div class="imgcap" align="middle">
+<center><img src="../../data/fetchPickAndPlaceContrast.png"></center>
+<div class="thecap" align="middle"><b>Training results for Fetch Pick and Place task constrasting between training with and without demonstration data.</b></div>
+</div>
+
diff --git a/baselines/baselines/her/__init__.py b/baselines/baselines/her/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/baselines/baselines/her/actor_critic.py b/baselines/baselines/her/actor_critic.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffd1c7f23228d78eb634fe91b25fe254dc5f2262
--- /dev/null
+++ b/baselines/baselines/her/actor_critic.py
@@ -0,0 +1,65 @@
+import tensorflow as tf
+from baselines.her.util import store_args, nn
+
+
+def normalize(x, stats):
+ if stats is None:
+ return x
+ return (x - stats.mean) / (stats.std + 1e-8)
+
+
+def denormalize(x, stats):
+ if stats is None:
+ return x
+ return x * stats.std + stats.mean
+
+class ActorCritic:
+ @store_args
+ def __init__(self, name, dimo, dimg, dimu, max_u, o_stats, g_stats, hidden, layers,
+ **kwargs):
+ """The actor-critic network and related training code.
+
+ Args:
+ dimo (int): the dimension of the observations
+ dimg (int): the dimension of the goals
+ dimu (int): the dimension of the actions
+ max_u (float): the maximum magnitude of actions; action outputs will be scaled
+ accordingly
+ o_stats (baselines.her.Normalizer): normalizer for observations
+ g_stats (baselines.her.Normalizer): normalizer for goals
+ hidden (int): number of hidden units that should be used in hidden layers
+ layers (int): number of hidden layers
+ """
+ # self.o_tf = inputs_tf['o']
+ # self.g_tf = inputs_tf['g']
+ # self.u_tf = inputs_tf['u']
+
+ # Prepare inputs for actor and critic.
+ # o = self.o_stats.normalize(self.o_tf)
+ # g = self.g_stats.normalize(self.g_tf)
+ # input_pi = tf.concat(axis=1, values=[o, g]) # for actor
+
+ input_pi_shape = dimo + dimg
+ self.actor_network = nn(
+ input_shape=input_pi_shape,
+ layers_sizes=[self.hidden] + self.layers + [self.dimu],
+ name='pi',
+ output_activation='tanh')
+ input_Q_shape = dimo + dimg + dimu
+ self.critic_network = nn(
+ input_shape=input_Q_shape,
+ layers_sizes=[self.hidden] + self.layers + [1],
+ name='Q')
+
+ # Networks.
+ # with tf.variable_scope('pi'):
+ # self.pi_tf = self.max_u * tf.tanh(nn(
+ # input_pi, [self.hidden] * self.layers + [self.dimu]))
+ # with tf.variable_scope('Q'):
+ # # for policy training
+ # input_Q = tf.concat(axis=1, values=[o, g, self.pi_tf / self.max_u])
+ # self.Q_pi_tf = nn(input_Q, [self.hidden] * self.layers + [1])
+ # # for critic training
+ # input_Q = tf.concat(axis=1, values=[o, g, self.u_tf / self.max_u])
+ # self._input_Q = input_Q # exposed for tests
+ # self.Q_tf = nn(input_Q, [self.hidden] * self.layers + [1], reuse=True)
diff --git a/baselines/baselines/her/ddpg.py b/baselines/baselines/her/ddpg.py
new file mode 100644
index 0000000000000000000000000000000000000000..c71828e14a84e7f914caaa469e8bde513880f57f
--- /dev/null
+++ b/baselines/baselines/her/ddpg.py
@@ -0,0 +1,413 @@
+from collections import OrderedDict
+
+import numpy as np
+import tensorflow as tf
+
+from baselines import logger
+from baselines.her.util import (
+ import_function, store_args, flatten_grads, transitions_in_episode_batch, convert_episode_to_batch_major)
+from baselines.her.normalizer import Normalizer
+from baselines.her.replay_buffer import ReplayBuffer
+from baselines.common.mpi_adam import MpiAdam
+from baselines.common import tf_util
+from baselines.common.mpi_running_mean_std import RunningMeanStd
+
+
+def dims_to_shapes(input_dims):
+ return {key: tuple([val]) if val > 0 else tuple() for key, val in input_dims.items()}
+
+
+global DEMO_BUFFER #buffer for demonstrations
+
+class DDPG(object):
+ @store_args
+ def __init__(self, input_dims, buffer_size, hidden, layers, network_class, polyak, batch_size,
+ Q_lr, pi_lr, norm_eps, norm_clip, max_u, action_l2, clip_obs, scope, T,
+ rollout_batch_size, subtract_goals, relative_goals, clip_pos_returns, clip_return,
+ bc_loss, q_filter, num_demo, demo_batch_size, prm_loss_weight, aux_loss_weight,
+ sample_transitions, gamma, reuse=False, **kwargs):
+ """Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
+ Added functionality to use demonstrations for training to Overcome exploration problem.
+
+ Args:
+ input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
+ actions (u)
+ buffer_size (int): number of transitions that are stored in the replay buffer
+ hidden (int): number of units in the hidden layers
+ layers (int): number of hidden layers
+ network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
+ polyak (float): coefficient for Polyak-averaging of the target network
+ batch_size (int): batch size for training
+ Q_lr (float): learning rate for the Q (critic) network
+ pi_lr (float): learning rate for the pi (actor) network
+ norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
+ norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
+ max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
+ action_l2 (float): coefficient for L2 penalty on the actions
+ clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
+ scope (str): the scope used for the TensorFlow graph
+ T (int): the time horizon for rollouts
+ rollout_batch_size (int): number of parallel rollouts per DDPG agent
+ subtract_goals (function): function that subtracts goals from each other
+ relative_goals (boolean): whether or not relative goals should be fed into the network
+ clip_pos_returns (boolean): whether or not positive returns should be clipped
+ clip_return (float): clip returns to be in [-clip_return, clip_return]
+ sample_transitions (function) function that samples from the replay buffer
+ gamma (float): gamma used for Q learning updates
+ reuse (boolean): whether or not the networks should be reused
+ bc_loss: whether or not the behavior cloning loss should be used as an auxilliary loss
+ q_filter: whether or not a filter on the q value update should be used when training with demonstartions
+ num_demo: Number of episodes in to be used in the demonstration buffer
+ demo_batch_size: number of samples to be used from the demonstrations buffer, per mpi thread
+ prm_loss_weight: Weight corresponding to the primary loss
+ aux_loss_weight: Weight corresponding to the auxilliary loss also called the cloning loss
+ """
+ if self.clip_return is None:
+ self.clip_return = np.inf
+
+ self.create_actor_critic = import_function(self.network_class)
+
+ input_shapes = dims_to_shapes(self.input_dims)
+ self.dimo = self.input_dims['o']
+ self.dimg = self.input_dims['g']
+ self.dimu = self.input_dims['u']
+
+ # Prepare staging area for feeding data to the model.
+ stage_shapes = OrderedDict()
+ for key in sorted(self.input_dims.keys()):
+ if key.startswith('info_'):
+ continue
+ stage_shapes[key] = (None, *input_shapes[key])
+ for key in ['o', 'g']:
+ stage_shapes[key + '_2'] = stage_shapes[key]
+ stage_shapes['r'] = (None,)
+ self.stage_shapes = stage_shapes
+ print('stage shapes are {}'.format(list(stage_shapes.values())))
+
+ # Create network.
+ with tf.variable_scope(self.scope):
+ self.staging_tf = StagingArea(
+ dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
+ shapes=list(self.stage_shapes.values()))
+ self.buffer_ph_tf = [
+ tf.placeholder(tf.float32, shape=shape) for shape in self.stage_shapes.values()]
+ self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
+
+ self._create_network(reuse=reuse)
+
+ # Configure the replay buffer.
+ buffer_shapes = {key: (self.T-1 if key != 'o' else self.T, *input_shapes[key])
+ for key, val in input_shapes.items()}
+ buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
+ buffer_shapes['ag'] = (self.T, self.dimg)
+
+ buffer_size = (self.buffer_size // self.rollout_batch_size) * self.rollout_batch_size
+ self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions)
+
+ global DEMO_BUFFER
+ DEMO_BUFFER = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions) #initialize the demo buffer; in the same way as the primary data buffer
+
+ def _random_action(self, n):
+ return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu))
+
+ def _preprocess_og(self, o, ag, g):
+ if self.relative_goals:
+ g_shape = g.shape
+ g = g.reshape(-1, self.dimg)
+ ag = ag.reshape(-1, self.dimg)
+ g = self.subtract_goals(g, ag)
+ g = g.reshape(*g_shape)
+ o = np.clip(o, -self.clip_obs, self.clip_obs)
+ g = np.clip(g, -self.clip_obs, self.clip_obs)
+ return o, g
+
+ def step(self, obs):
+ actions = self.get_actions(obs['observation'], obs['achieved_goal'], obs['desired_goal'])
+ return actions, None, None, None
+
+
+ def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=False,
+ compute_Q=False):
+ o, g = self._preprocess_og(o, ag, g)
+ policy = self.target if use_target_net else self.main
+ # values to compute
+ vals = [policy.pi_tf]
+ if compute_Q:
+ vals += [policy.Q_pi_tf]
+ # feed
+ feed = {
+ policy.o_tf: o.reshape(-1, self.dimo),
+ policy.g_tf: g.reshape(-1, self.dimg),
+ policy.u_tf: np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
+ }
+
+ ret = self.sess.run(vals, feed_dict=feed)
+ # action postprocessing
+ u = ret[0]
+ noise = noise_eps * self.max_u * np.random.randn(*u.shape) # gaussian noise
+ u += noise
+ u = np.clip(u, -self.max_u, self.max_u)
+ u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (self._random_action(u.shape[0]) - u) # eps-greedy
+ if u.shape[0] == 1:
+ u = u[0]
+ u = u.copy()
+ ret[0] = u
+
+ if len(ret) == 1:
+ return ret[0]
+ else:
+ return ret
+
+ def init_demo_buffer(self, demoDataFile, update_stats=True): #function that initializes the demo buffer
+
+ demoData = np.load(demoDataFile) #load the demonstration data from data file
+ info_keys = [key.replace('info_', '') for key in self.input_dims.keys() if key.startswith('info_')]
+ info_values = [np.empty((self.T - 1, 1, self.input_dims['info_' + key]), np.float32) for key in info_keys]
+
+ demo_data_obs = demoData['obs']
+ demo_data_acs = demoData['acs']
+ demo_data_info = demoData['info']
+
+ for epsd in range(self.num_demo): # we initialize the whole demo buffer at the start of the training
+ obs, acts, goals, achieved_goals = [], [] ,[] ,[]
+ i = 0
+ for transition in range(self.T - 1):
+ obs.append([demo_data_obs[epsd][transition].get('observation')])
+ acts.append([demo_data_acs[epsd][transition]])
+ goals.append([demo_data_obs[epsd][transition].get('desired_goal')])
+ achieved_goals.append([demo_data_obs[epsd][transition].get('achieved_goal')])
+ for idx, key in enumerate(info_keys):
+ info_values[idx][transition, i] = demo_data_info[epsd][transition][key]
+
+
+ obs.append([demo_data_obs[epsd][self.T - 1].get('observation')])
+ achieved_goals.append([demo_data_obs[epsd][self.T - 1].get('achieved_goal')])
+
+ episode = dict(o=obs,
+ u=acts,
+ g=goals,
+ ag=achieved_goals)
+ for key, value in zip(info_keys, info_values):
+ episode['info_{}'.format(key)] = value
+
+ episode = convert_episode_to_batch_major(episode)
+ global DEMO_BUFFER
+ DEMO_BUFFER.store_episode(episode) # create the observation dict and append them into the demonstration buffer
+ logger.debug("Demo buffer size currently ", DEMO_BUFFER.get_current_size()) #print out the demonstration buffer size
+
+ if update_stats:
+ # add transitions to normalizer to normalize the demo data as well
+ episode['o_2'] = episode['o'][:, 1:, :]
+ episode['ag_2'] = episode['ag'][:, 1:, :]
+ num_normalizing_transitions = transitions_in_episode_batch(episode)
+ transitions = self.sample_transitions(episode, num_normalizing_transitions)
+
+ o, g, ag = transitions['o'], transitions['g'], transitions['ag']
+ transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
+ # No need to preprocess the o_2 and g_2 since this is only used for stats
+
+ self.o_stats.update(transitions['o'])
+ self.g_stats.update(transitions['g'])
+
+ self.o_stats.recompute_stats()
+ self.g_stats.recompute_stats()
+ episode.clear()
+
+ logger.info("Demo buffer size: ", DEMO_BUFFER.get_current_size()) #print out the demonstration buffer size
+
+ def store_episode(self, episode_batch, update_stats=True):
+ """
+ episode_batch: array of batch_size x (T or T+1) x dim_key
+ 'o' is of size T+1, others are of size T
+ """
+
+ self.buffer.store_episode(episode_batch)
+
+ if update_stats:
+ # add transitions to normalizer
+ episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
+ episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
+ num_normalizing_transitions = transitions_in_episode_batch(episode_batch)
+ transitions = self.sample_transitions(episode_batch, num_normalizing_transitions)
+
+ o, g, ag = transitions['o'], transitions['g'], transitions['ag']
+ transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
+ # No need to preprocess the o_2 and g_2 since this is only used for stats
+
+ self.o_stats.update(transitions['o'])
+ self.g_stats.update(transitions['g'])
+
+ self.o_stats.recompute_stats()
+ self.g_stats.recompute_stats()
+
+ def get_current_buffer_size(self):
+ return self.buffer.get_current_size()
+
+ def _sync_optimizers(self):
+ self.Q_adam.sync()
+ self.pi_adam.sync()
+
+ def _grads(self):
+ # Avoid feed_dict here for performance!
+ critic_loss, actor_loss, Q_grad, pi_grad = self.sess.run([
+ self.Q_loss_tf,
+ self.main.Q_pi_tf,
+ self.Q_grad_tf,
+ self.pi_grad_tf
+ ])
+ return critic_loss, actor_loss, Q_grad, pi_grad
+
+ def _update(self, Q_grad, pi_grad):
+ self.Q_adam.update(Q_grad, self.Q_lr)
+ self.pi_adam.update(pi_grad, self.pi_lr)
+
+ def sample_batch(self):
+ if self.bc_loss: #use demonstration buffer to sample as well if bc_loss flag is set TRUE
+ transitions = self.buffer.sample(self.batch_size - self.demo_batch_size)
+ global DEMO_BUFFER
+ transitions_demo = DEMO_BUFFER.sample(self.demo_batch_size) #sample from the demo buffer
+ for k, values in transitions_demo.items():
+ rolloutV = transitions[k].tolist()
+ for v in values:
+ rolloutV.append(v.tolist())
+ transitions[k] = np.array(rolloutV)
+ else:
+ transitions = self.buffer.sample(self.batch_size) #otherwise only sample from primary buffer
+
+ o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
+ ag, ag_2 = transitions['ag'], transitions['ag_2']
+ transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
+ transitions['o_2'], transitions['g_2'] = self._preprocess_og(o_2, ag_2, g)
+
+ transitions_batch = [transitions[key] for key in self.stage_shapes.keys()]
+ return transitions_batch
+
+ def stage_batch(self, batch=None):
+ if batch is None:
+ batch = self.sample_batch()
+ assert len(self.buffer_ph_tf) == len(batch)
+ self.sess.run(self.stage_op, feed_dict=dict(zip(self.buffer_ph_tf, batch)))
+
+ def train(self, stage=True):
+ if stage:
+ self.stage_batch()
+ critic_loss, actor_loss, Q_grad, pi_grad = self._grads()
+ self._update(Q_grad, pi_grad)
+ return critic_loss, actor_loss
+
+ def _init_target_net(self):
+ for var, target_var in zip(self.main.trainable_variables, self.target.trainable_variables):
+ target_var.assign(var)
+
+ def update_target_net(self):
+ for var, target_var in zip(self.main.trainable_variables, self.target.trainable_variables):
+ target_var.assign(self.polyak * target_var + (1. - self.polyak * var))
+
+ def clear_buffer(self):
+ self.buffer.clear_buffer()
+
+ def _create_network(self, reuse=False):
+ logger.info("Creating a DDPG agent with action space %d x %s..." % (self.dimu, self.max_u))
+
+ # running averages
+ with tf.name_scope('o_stats'):
+ self.o_stats = RunningMeanStd(epsilon=self.norm_eps, shape=(self.dimo,), default_clip_range=self.norm_clip)
+ with tf.name_scope('g_stats'):
+ self.g_stats = RunningMeanStd(epsilon=self.norm_eps, shape=(self.dimg,), default_clip_range=self.norm_clip)
+
+ # mini-batch sampling.
+ batch = self.staging_tf.get()
+ batch_tf = OrderedDict([(key, batch[i])
+ for i, key in enumerate(self.stage_shapes.keys())])
+ batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
+
+ #choose only the demo buffer samples
+ mask = np.concatenate((np.zeros(self.batch_size - self.demo_batch_size), np.ones(self.demo_batch_size)), axis = 0)
+
+ self.main = self.create_actor_critic(name='main', **self.__dict__)
+ self.target = self.create_actor_critic(name='target', **self.__dict__)
+
+ # loss functions
+ # target_Q_pi_tf = self.target.Q_pi_tf
+ # clip_range = (-self.clip_return, 0. if self.clip_pos_returns else np.inf)
+ # target_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range)
+ # self.Q_loss_tf = tf.reduce_mean(tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf))
+
+ # if self.bc_loss ==1 and self.q_filter == 1 : # train with demonstrations and use bc_loss and q_filter both
+ # maskMain = tf.reshape(tf.boolean_mask(self.main.Q_tf > self.main.Q_pi_tf, mask), [-1]) #where is the demonstrator action better than actor action according to the critic? choose those samples only
+ # #define the cloning loss on the actor's actions only on the samples which adhere to the above masks
+ # self.cloning_loss_tf = tf.reduce_sum(tf.square(tf.boolean_mask(tf.boolean_mask((self.main.pi_tf), mask), maskMain, axis=0) - tf.boolean_mask(tf.boolean_mask((batch_tf['u']), mask), maskMain, axis=0)))
+ # self.pi_loss_tf = -self.prm_loss_weight * tf.reduce_mean(self.main.Q_pi_tf) #primary loss scaled by it's respective weight prm_loss_weight
+ # self.pi_loss_tf += self.prm_loss_weight * self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u)) #L2 loss on action values scaled by the same weight prm_loss_weight
+ # self.pi_loss_tf += self.aux_loss_weight * self.cloning_loss_tf #adding the cloning loss to the actor loss as an auxilliary loss scaled by its weight aux_loss_weight
+
+ # elif self.bc_loss == 1 and self.q_filter == 0: # train with demonstrations without q_filter
+ # self.cloning_loss_tf = tf.reduce_sum(tf.square(tf.boolean_mask((self.main.pi_tf), mask) - tf.boolean_mask((batch_tf['u']), mask)))
+ # self.pi_loss_tf = -self.prm_loss_weight * tf.reduce_mean(self.main.Q_pi_tf)
+ # self.pi_loss_tf += self.prm_loss_weight * self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
+ # self.pi_loss_tf += self.aux_loss_weight * self.cloning_loss_tf
+
+ # else: #If not training with demonstrations
+ # self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
+ # self.pi_loss_tf += self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
+
+ # Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
+ # pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
+
+ # self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q'))
+ # self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
+ # self.Q_grad_tf = flatten_grads(grads=Q_grads_tf, var_list=self._vars('main/Q'))
+ # self.pi_grad_tf = flatten_grads(grads=pi_grads_tf, var_list=self._vars('main/pi'))
+
+ # optimizers
+ self.Q_adam = MpiAdam(self.main.critic_network.trainable_variables, scale_grad_by_procs=False)
+ self.pi_adam = MpiAdam(self.main.actor_network.trainable_variables, scale_grad_by_procs=False)
+
+ # initialize all variables
+ self._sync_optimizers()
+ self._init_target_net()
+
+ def logs(self, prefix=''):
+ logs = []
+ logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
+ logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
+ logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
+ logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
+
+ if prefix != '' and not prefix.endswith('/'):
+ return [(prefix + '/' + key, val) for key, val in logs]
+ else:
+ return logs
+
+ def __getstate__(self):
+ """Our policies can be loaded from pkl, but after unpickling you cannot continue training.
+ """
+ excluded_subnames = ['_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats',
+ 'main', 'target', 'lock', 'env', 'sample_transitions',
+ 'stage_shapes', 'create_actor_critic']
+
+ state = {k: v for k, v in self.__dict__.items() if all([not subname in k for subname in excluded_subnames])}
+ state['buffer_size'] = self.buffer_size
+ state['tf'] = self.sess.run([x for x in self._global_vars('') if 'buffer' not in x.name])
+ return state
+
+ def __setstate__(self, state):
+ if 'sample_transitions' not in state:
+ # We don't need this for playing the policy.
+ state['sample_transitions'] = None
+
+ self.__init__(**state)
+ # set up stats (they are overwritten in __init__)
+ for k, v in state.items():
+ if k[-6:] == '_stats':
+ self.__dict__[k] = v
+ # load TF variables
+ vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
+ assert(len(vars) == len(state["tf"]))
+ node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
+ self.sess.run(node)
+
+ #TODO: add saving logic
+ # def save(self, save_path):
+ # tf_util.save_variables(save_path)
+
diff --git a/baselines/baselines/her/experiment/__init__.py b/baselines/baselines/her/experiment/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/baselines/baselines/her/experiment/config.py b/baselines/baselines/her/experiment/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..6370505a844783a36c6866e70cf3b42fe9c23936
--- /dev/null
+++ b/baselines/baselines/her/experiment/config.py
@@ -0,0 +1,201 @@
+import os
+import numpy as np
+import gym
+
+from baselines import logger
+from baselines.her.ddpg import DDPG
+from baselines.her.her_sampler import make_sample_her_transitions
+from baselines.bench.monitor import Monitor
+
+DEFAULT_ENV_PARAMS = {
+ 'FetchReach-v1': {
+ 'n_cycles': 10,
+ },
+}
+
+
+DEFAULT_PARAMS = {
+ # env
+ 'max_u': 1., # max absolute value of actions on different coordinates
+ # ddpg
+ 'layers': 3, # number of layers in the critic/actor networks
+ 'hidden': 256, # number of neurons in each hidden layers
+ 'network_class': 'baselines.her.actor_critic:ActorCritic',
+ 'Q_lr': 0.001, # critic learning rate
+ 'pi_lr': 0.001, # actor learning rate
+ 'buffer_size': int(1E6), # for experience replay
+ 'polyak': 0.95, # polyak averaging coefficient
+ 'action_l2': 1.0, # quadratic penalty on actions (before rescaling by max_u)
+ 'clip_obs': 200.,
+ 'scope': 'ddpg', # can be tweaked for testing
+ 'relative_goals': False,
+ # training
+ 'n_cycles': 50, # per epoch
+ 'rollout_batch_size': 2, # per mpi thread
+ 'n_batches': 40, # training batches per cycle
+ 'batch_size': 256, # per mpi thread, measured in transitions and reduced to even multiple of chunk_length.
+ 'n_test_rollouts': 10, # number of test rollouts per epoch, each consists of rollout_batch_size rollouts
+ 'test_with_polyak': False, # run test episodes with the target network
+ # exploration
+ 'random_eps': 0.3, # percentage of time a random action is taken
+ 'noise_eps': 0.2, # std of gaussian noise added to not-completely-random actions as a percentage of max_u
+ # HER
+ 'replay_strategy': 'future', # supported modes: future, none
+ 'replay_k': 4, # number of additional goals used for replay, only used if off_policy_data=future
+ # normalization
+ 'norm_eps': 0.01, # epsilon used for observation normalization
+ 'norm_clip': 5, # normalized observations are cropped to this values
+
+ 'bc_loss': 0, # whether or not to use the behavior cloning loss as an auxilliary loss
+ 'q_filter': 0, # whether or not a Q value filter should be used on the Actor outputs
+ 'num_demo': 100, # number of expert demo episodes
+ 'demo_batch_size': 128, #number of samples to be used from the demonstrations buffer, per mpi thread 128/1024 or 32/256
+ 'prm_loss_weight': 0.001, #Weight corresponding to the primary loss
+ 'aux_loss_weight': 0.0078, #Weight corresponding to the auxilliary loss also called the cloning loss
+}
+
+
+CACHED_ENVS = {}
+
+
+def cached_make_env(make_env):
+ """
+ Only creates a new environment from the provided function if one has not yet already been
+ created. This is useful here because we need to infer certain properties of the env, e.g.
+ its observation and action spaces, without any intend of actually using it.
+ """
+ if make_env not in CACHED_ENVS:
+ env = make_env()
+ CACHED_ENVS[make_env] = env
+ return CACHED_ENVS[make_env]
+
+
+def prepare_params(kwargs):
+ # DDPG params
+ ddpg_params = dict()
+ env_name = kwargs['env_name']
+
+ def make_env(subrank=None):
+ env = gym.make(env_name)
+ if subrank is not None and logger.get_dir() is not None:
+ try:
+ from mpi4py import MPI
+ mpi_rank = MPI.COMM_WORLD.Get_rank()
+ except ImportError:
+ MPI = None
+ mpi_rank = 0
+ logger.warn('Running with a single MPI process. This should work, but the results may differ from the ones publshed in Plappert et al.')
+
+ max_episode_steps = env._max_episode_steps
+ env = Monitor(env,
+ os.path.join(logger.get_dir(), str(mpi_rank) + '.' + str(subrank)),
+ allow_early_resets=True)
+ # hack to re-expose _max_episode_steps (ideally should replace reliance on it downstream)
+ env = gym.wrappers.TimeLimit(env, max_episode_steps=max_episode_steps)
+ return env
+
+ kwargs['make_env'] = make_env
+ tmp_env = cached_make_env(kwargs['make_env'])
+ assert hasattr(tmp_env, '_max_episode_steps')
+ kwargs['T'] = tmp_env._max_episode_steps
+
+ kwargs['max_u'] = np.array(kwargs['max_u']) if isinstance(kwargs['max_u'], list) else kwargs['max_u']
+ kwargs['gamma'] = 1. - 1. / kwargs['T']
+ if 'lr' in kwargs:
+ kwargs['pi_lr'] = kwargs['lr']
+ kwargs['Q_lr'] = kwargs['lr']
+ del kwargs['lr']
+ for name in ['buffer_size', 'hidden', 'layers',
+ 'network_class',
+ 'polyak',
+ 'batch_size', 'Q_lr', 'pi_lr',
+ 'norm_eps', 'norm_clip', 'max_u',
+ 'action_l2', 'clip_obs', 'scope', 'relative_goals']:
+ ddpg_params[name] = kwargs[name]
+ kwargs['_' + name] = kwargs[name]
+ del kwargs[name]
+ kwargs['ddpg_params'] = ddpg_params
+
+ return kwargs
+
+
+def log_params(params, logger=logger):
+ for key in sorted(params.keys()):
+ logger.info('{}: {}'.format(key, params[key]))
+
+
+def configure_her(params):
+ env = cached_make_env(params['make_env'])
+ env.reset()
+
+ def reward_fun(ag_2, g, info): # vectorized
+ return env.compute_reward(achieved_goal=ag_2, desired_goal=g, info=info)
+
+ # Prepare configuration for HER.
+ her_params = {
+ 'reward_fun': reward_fun,
+ }
+ for name in ['replay_strategy', 'replay_k']:
+ her_params[name] = params[name]
+ params['_' + name] = her_params[name]
+ del params[name]
+ sample_her_transitions = make_sample_her_transitions(**her_params)
+
+ return sample_her_transitions
+
+
+def simple_goal_subtract(a, b):
+ assert a.shape == b.shape
+ return a - b
+
+
+def configure_ddpg(dims, params, reuse=False, use_mpi=True, clip_return=True):
+ sample_her_transitions = configure_her(params)
+ # Extract relevant parameters.
+ gamma = params['gamma']
+ rollout_batch_size = params['rollout_batch_size']
+ ddpg_params = params['ddpg_params']
+
+ input_dims = dims.copy()
+
+ # DDPG agent
+ env = cached_make_env(params['make_env'])
+ env.reset()
+ ddpg_params.update({'input_dims': input_dims, # agent takes an input observations
+ 'T': params['T'],
+ 'clip_pos_returns': True, # clip positive returns
+ 'clip_return': (1. / (1. - gamma)) if clip_return else np.inf, # max abs of return
+ 'rollout_batch_size': rollout_batch_size,
+ 'subtract_goals': simple_goal_subtract,
+ 'sample_transitions': sample_her_transitions,
+ 'gamma': gamma,
+ 'bc_loss': params['bc_loss'],
+ 'q_filter': params['q_filter'],
+ 'num_demo': params['num_demo'],
+ 'demo_batch_size': params['demo_batch_size'],
+ 'prm_loss_weight': params['prm_loss_weight'],
+ 'aux_loss_weight': params['aux_loss_weight'],
+ })
+ ddpg_params['info'] = {
+ 'env_name': params['env_name'],
+ }
+ policy = DDPG(reuse=reuse, **ddpg_params, use_mpi=use_mpi)
+ return policy
+
+
+def configure_dims(params):
+ env = cached_make_env(params['make_env'])
+ env.reset()
+ obs, _, _, info = env.step(env.action_space.sample())
+
+ dims = {
+ 'o': obs['observation'].shape[0],
+ 'u': env.action_space.shape[0],
+ 'g': obs['desired_goal'].shape[0],
+ }
+ for key, value in info.items():
+ value = np.array(value)
+ if value.ndim == 0:
+ value = value.reshape(1)
+ dims['info_{}'.format(key)] = value.shape[0]
+ return dims
diff --git a/baselines/baselines/her/experiment/data_generation/fetch_data_generation.py b/baselines/baselines/her/experiment/data_generation/fetch_data_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a0a755db120e86439770a8a52715ba6029e2dc7
--- /dev/null
+++ b/baselines/baselines/her/experiment/data_generation/fetch_data_generation.py
@@ -0,0 +1,126 @@
+import gym
+import numpy as np
+
+
+"""Data generation for the case of a single block pick and place in Fetch Env"""
+
+actions = []
+observations = []
+infos = []
+
+def main():
+ env = gym.make('FetchPickAndPlace-v1')
+ numItr = 100
+ initStateSpace = "random"
+ env.reset()
+ print("Reset!")
+ while len(actions) < numItr:
+ obs = env.reset()
+ print("ITERATION NUMBER ", len(actions))
+ goToGoal(env, obs)
+
+
+ fileName = "data_fetch"
+ fileName += "_" + initStateSpace
+ fileName += "_" + str(numItr)
+ fileName += ".npz"
+
+ np.savez_compressed(fileName, acs=actions, obs=observations, info=infos) # save the file
+
+def goToGoal(env, lastObs):
+
+ goal = lastObs['desired_goal']
+ objectPos = lastObs['observation'][3:6]
+ object_rel_pos = lastObs['observation'][6:9]
+ episodeAcs = []
+ episodeObs = []
+ episodeInfo = []
+
+ object_oriented_goal = object_rel_pos.copy()
+ object_oriented_goal[2] += 0.03 # first make the gripper go slightly above the object
+
+ timeStep = 0 #count the total number of timesteps
+ episodeObs.append(lastObs)
+
+ while np.linalg.norm(object_oriented_goal) >= 0.005 and timeStep <= env._max_episode_steps:
+ env.render()
+ action = [0, 0, 0, 0]
+ object_oriented_goal = object_rel_pos.copy()
+ object_oriented_goal[2] += 0.03
+
+ for i in range(len(object_oriented_goal)):
+ action[i] = object_oriented_goal[i]*6
+
+ action[len(action)-1] = 0.05 #open
+
+ obsDataNew, reward, done, info = env.step(action)
+ timeStep += 1
+
+ episodeAcs.append(action)
+ episodeInfo.append(info)
+ episodeObs.append(obsDataNew)
+
+ objectPos = obsDataNew['observation'][3:6]
+ object_rel_pos = obsDataNew['observation'][6:9]
+
+ while np.linalg.norm(object_rel_pos) >= 0.005 and timeStep <= env._max_episode_steps :
+ env.render()
+ action = [0, 0, 0, 0]
+ for i in range(len(object_rel_pos)):
+ action[i] = object_rel_pos[i]*6
+
+ action[len(action)-1] = -0.005
+
+ obsDataNew, reward, done, info = env.step(action)
+ timeStep += 1
+
+ episodeAcs.append(action)
+ episodeInfo.append(info)
+ episodeObs.append(obsDataNew)
+
+ objectPos = obsDataNew['observation'][3:6]
+ object_rel_pos = obsDataNew['observation'][6:9]
+
+
+ while np.linalg.norm(goal - objectPos) >= 0.01 and timeStep <= env._max_episode_steps :
+ env.render()
+ action = [0, 0, 0, 0]
+ for i in range(len(goal - objectPos)):
+ action[i] = (goal - objectPos)[i]*6
+
+ action[len(action)-1] = -0.005
+
+ obsDataNew, reward, done, info = env.step(action)
+ timeStep += 1
+
+ episodeAcs.append(action)
+ episodeInfo.append(info)
+ episodeObs.append(obsDataNew)
+
+ objectPos = obsDataNew['observation'][3:6]
+ object_rel_pos = obsDataNew['observation'][6:9]
+
+ while True: #limit the number of timesteps in the episode to a fixed duration
+ env.render()
+ action = [0, 0, 0, 0]
+ action[len(action)-1] = -0.005 # keep the gripper closed
+
+ obsDataNew, reward, done, info = env.step(action)
+ timeStep += 1
+
+ episodeAcs.append(action)
+ episodeInfo.append(info)
+ episodeObs.append(obsDataNew)
+
+ objectPos = obsDataNew['observation'][3:6]
+ object_rel_pos = obsDataNew['observation'][6:9]
+
+ if timeStep >= env._max_episode_steps: break
+
+ actions.append(episodeAcs)
+ observations.append(episodeObs)
+ infos.append(episodeInfo)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/baselines/baselines/her/experiment/play.py b/baselines/baselines/her/experiment/play.py
new file mode 100644
index 0000000000000000000000000000000000000000..8989c9277ecd7cdf3781df1d90a4d3b4e7b43099
--- /dev/null
+++ b/baselines/baselines/her/experiment/play.py
@@ -0,0 +1,61 @@
+# DEPRECATED, use --play flag to baselines.run instead
+import click
+import numpy as np
+import pickle
+
+from baselines import logger
+from baselines.common import set_global_seeds
+import baselines.her.experiment.config as config
+from baselines.her.rollout import RolloutWorker
+
+
+@click.command()
+@click.argument('policy_file', type=str)
+@click.option('--seed', type=int, default=0)
+@click.option('--n_test_rollouts', type=int, default=10)
+@click.option('--render', type=int, default=1)
+def main(policy_file, seed, n_test_rollouts, render):
+ set_global_seeds(seed)
+
+ # Load policy.
+ with open(policy_file, 'rb') as f:
+ policy = pickle.load(f)
+ env_name = policy.info['env_name']
+
+ # Prepare params.
+ params = config.DEFAULT_PARAMS
+ if env_name in config.DEFAULT_ENV_PARAMS:
+ params.update(config.DEFAULT_ENV_PARAMS[env_name]) # merge env-specific parameters in
+ params['env_name'] = env_name
+ params = config.prepare_params(params)
+ config.log_params(params, logger=logger)
+
+ dims = config.configure_dims(params)
+
+ eval_params = {
+ 'exploit': True,
+ 'use_target_net': params['test_with_polyak'],
+ 'compute_Q': True,
+ 'rollout_batch_size': 1,
+ 'render': bool(render),
+ }
+
+ for name in ['T', 'gamma', 'noise_eps', 'random_eps']:
+ eval_params[name] = params[name]
+
+ evaluator = RolloutWorker(params['make_env'], policy, dims, logger, **eval_params)
+ evaluator.seed(seed)
+
+ # Run evaluation.
+ evaluator.clear_history()
+ for _ in range(n_test_rollouts):
+ evaluator.generate_rollouts()
+
+ # record logs
+ for key, val in evaluator.logs('test'):
+ logger.record_tabular(key, np.mean(val))
+ logger.dump_tabular()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/baselines/baselines/her/experiment/plot.py b/baselines/baselines/her/experiment/plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..542de0e1ac5a518d824addc30ec3271ea4f5656a
--- /dev/null
+++ b/baselines/baselines/her/experiment/plot.py
@@ -0,0 +1,120 @@
+# DEPRECATED, use baselines.common.plot_util instead
+
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import json
+import seaborn as sns; sns.set()
+import glob2
+import argparse
+
+
+def smooth_reward_curve(x, y):
+ halfwidth = int(np.ceil(len(x) / 60)) # Halfwidth of our smoothing convolution
+ k = halfwidth
+ xsmoo = x
+ ysmoo = np.convolve(y, np.ones(2 * k + 1), mode='same') / np.convolve(np.ones_like(y), np.ones(2 * k + 1),
+ mode='same')
+ return xsmoo, ysmoo
+
+
+def load_results(file):
+ if not os.path.exists(file):
+ return None
+ with open(file, 'r') as f:
+ lines = [line for line in f]
+ if len(lines) < 2:
+ return None
+ keys = [name.strip() for name in lines[0].split(',')]
+ data = np.genfromtxt(file, delimiter=',', skip_header=1, filling_values=0.)
+ if data.ndim == 1:
+ data = data.reshape(1, -1)
+ assert data.ndim == 2
+ assert data.shape[-1] == len(keys)
+ result = {}
+ for idx, key in enumerate(keys):
+ result[key] = data[:, idx]
+ return result
+
+
+def pad(xs, value=np.nan):
+ maxlen = np.max([len(x) for x in xs])
+
+ padded_xs = []
+ for x in xs:
+ if x.shape[0] >= maxlen:
+ padded_xs.append(x)
+
+ padding = np.ones((maxlen - x.shape[0],) + x.shape[1:]) * value
+ x_padded = np.concatenate([x, padding], axis=0)
+ assert x_padded.shape[1:] == x.shape[1:]
+ assert x_padded.shape[0] == maxlen
+ padded_xs.append(x_padded)
+ return np.array(padded_xs)
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('dir', type=str)
+parser.add_argument('--smooth', type=int, default=1)
+args = parser.parse_args()
+
+# Load all data.
+data = {}
+paths = [os.path.abspath(os.path.join(path, '..')) for path in glob2.glob(os.path.join(args.dir, '**', 'progress.csv'))]
+for curr_path in paths:
+ if not os.path.isdir(curr_path):
+ continue
+ results = load_results(os.path.join(curr_path, 'progress.csv'))
+ if not results:
+ print('skipping {}'.format(curr_path))
+ continue
+ print('loading {} ({})'.format(curr_path, len(results['epoch'])))
+ with open(os.path.join(curr_path, 'params.json'), 'r') as f:
+ params = json.load(f)
+
+ success_rate = np.array(results['test/success_rate'])
+ epoch = np.array(results['epoch']) + 1
+ env_id = params['env_name']
+ replay_strategy = params['replay_strategy']
+
+ if replay_strategy == 'future':
+ config = 'her'
+ else:
+ config = 'ddpg'
+ if 'Dense' in env_id:
+ config += '-dense'
+ else:
+ config += '-sparse'
+ env_id = env_id.replace('Dense', '')
+
+ # Process and smooth data.
+ assert success_rate.shape == epoch.shape
+ x = epoch
+ y = success_rate
+ if args.smooth:
+ x, y = smooth_reward_curve(epoch, success_rate)
+ assert x.shape == y.shape
+
+ if env_id not in data:
+ data[env_id] = {}
+ if config not in data[env_id]:
+ data[env_id][config] = []
+ data[env_id][config].append((x, y))
+
+# Plot data.
+for env_id in sorted(data.keys()):
+ print('exporting {}'.format(env_id))
+ plt.clf()
+
+ for config in sorted(data[env_id].keys()):
+ xs, ys = zip(*data[env_id][config])
+ xs, ys = pad(xs), pad(ys)
+ assert xs.shape == ys.shape
+
+ plt.plot(xs[0], np.nanmedian(ys, axis=0), label=config)
+ plt.fill_between(xs[0], np.nanpercentile(ys, 25, axis=0), np.nanpercentile(ys, 75, axis=0), alpha=0.25)
+ plt.title(env_id)
+ plt.xlabel('Epoch')
+ plt.ylabel('Median Success Rate')
+ plt.legend()
+ plt.savefig(os.path.join(args.dir, 'fig_{}.png'.format(env_id)))
diff --git a/baselines/baselines/her/her.py b/baselines/baselines/her/her.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f7d893bb1bf6b4ef3d75fa374551d7fd592d876
--- /dev/null
+++ b/baselines/baselines/her/her.py
@@ -0,0 +1,196 @@
+import os
+
+import click
+import numpy as np
+import json
+from mpi4py import MPI
+
+from baselines import logger
+from baselines.common import set_global_seeds, tf_util
+from baselines.common.mpi_moments import mpi_moments
+import baselines.her.experiment.config as config
+from baselines.her.rollout import RolloutWorker
+
+def mpi_average(value):
+ if not isinstance(value, list):
+ value = [value]
+ if not any(value):
+ value = [0.]
+ return mpi_moments(np.array(value))[0]
+
+
+def train(*, policy, rollout_worker, evaluator,
+ n_epochs, n_test_rollouts, n_cycles, n_batches, policy_save_interval,
+ save_path, demo_file, **kwargs):
+ rank = MPI.COMM_WORLD.Get_rank()
+
+ if save_path:
+ latest_policy_path = os.path.join(save_path, 'policy_latest.pkl')
+ best_policy_path = os.path.join(save_path, 'policy_best.pkl')
+ periodic_policy_path = os.path.join(save_path, 'policy_{}.pkl')
+
+ logger.info("Training...")
+ best_success_rate = -1
+
+ if policy.bc_loss == 1: policy.init_demo_buffer(demo_file) #initialize demo buffer if training with demonstrations
+
+ # num_timesteps = n_epochs * n_cycles * rollout_length * number of rollout workers
+ for epoch in range(n_epochs):
+ # train
+ rollout_worker.clear_history()
+ for _ in range(n_cycles):
+ episode = rollout_worker.generate_rollouts()
+ policy.store_episode(episode)
+ for _ in range(n_batches):
+ policy.train()
+ policy.update_target_net()
+
+ # test
+ evaluator.clear_history()
+ for _ in range(n_test_rollouts):
+ evaluator.generate_rollouts()
+
+ # record logs
+ logger.record_tabular('epoch', epoch)
+ for key, val in evaluator.logs('test'):
+ logger.record_tabular(key, mpi_average(val))
+ for key, val in rollout_worker.logs('train'):
+ logger.record_tabular(key, mpi_average(val))
+ for key, val in policy.logs():
+ logger.record_tabular(key, mpi_average(val))
+
+ if rank == 0:
+ logger.dump_tabular()
+
+ # save the policy if it's better than the previous ones
+ success_rate = mpi_average(evaluator.current_success_rate())
+ if rank == 0 and success_rate >= best_success_rate and save_path:
+ best_success_rate = success_rate
+ logger.info('New best success rate: {}. Saving policy to {} ...'.format(best_success_rate, best_policy_path))
+ evaluator.save_policy(best_policy_path)
+ evaluator.save_policy(latest_policy_path)
+ if rank == 0 and policy_save_interval > 0 and epoch % policy_save_interval == 0 and save_path:
+ policy_path = periodic_policy_path.format(epoch)
+ logger.info('Saving periodic policy to {} ...'.format(policy_path))
+ evaluator.save_policy(policy_path)
+
+ # make sure that different threads have different seeds
+ local_uniform = np.random.uniform(size=(1,))
+ root_uniform = local_uniform.copy()
+ MPI.COMM_WORLD.Bcast(root_uniform, root=0)
+ if rank != 0:
+ assert local_uniform[0] != root_uniform[0]
+
+ return policy
+
+
+def learn(*, network, env, total_timesteps,
+ seed=None,
+ eval_env=None,
+ replay_strategy='future',
+ policy_save_interval=5,
+ clip_return=True,
+ demo_file=None,
+ override_params=None,
+ load_path=None,
+ save_path=None,
+ **kwargs
+):
+
+ raise TypeError('HER is not supported in TF2 branch yet, we are still working on it.')
+ override_params = override_params or {}
+ if MPI is not None:
+ rank = MPI.COMM_WORLD.Get_rank()
+ num_cpu = MPI.COMM_WORLD.Get_size()
+
+ # Seed everything.
+ rank_seed = seed + 1000000 * rank if seed is not None else None
+ set_global_seeds(rank_seed)
+
+ # Prepare params.
+ params = config.DEFAULT_PARAMS
+ env_name = env.spec.id
+ params['env_name'] = env_name
+ params['replay_strategy'] = replay_strategy
+ if env_name in config.DEFAULT_ENV_PARAMS:
+ params.update(config.DEFAULT_ENV_PARAMS[env_name]) # merge env-specific parameters in
+ params.update(**override_params) # makes it possible to override any parameter
+ with open(os.path.join(logger.get_dir(), 'params.json'), 'w') as f:
+ json.dump(params, f)
+ params = config.prepare_params(params)
+ params['rollout_batch_size'] = env.num_envs
+
+ if demo_file is not None:
+ params['bc_loss'] = 1
+ params.update(kwargs)
+
+ config.log_params(params, logger=logger)
+
+ if num_cpu == 1:
+ logger.warn()
+ logger.warn('*** Warning ***')
+ logger.warn(
+ 'You are running HER with just a single MPI worker. This will work, but the ' +
+ 'experiments that we report in Plappert et al. (2018, https://arxiv.org/abs/1802.09464) ' +
+ 'were obtained with --num_cpu 19. This makes a significant difference and if you ' +
+ 'are looking to reproduce those results, be aware of this. Please also refer to ' +
+ 'https://github.com/openai/baselines/issues/314 for further details.')
+ logger.warn('****************')
+ logger.warn()
+
+ dims = config.configure_dims(params)
+ print('dims are {}'.format(dims))
+ policy = config.configure_ddpg(dims=dims, params=params, clip_return=clip_return)
+ #TODO: load path
+ # if load_path is not None:
+ # tf_util.load_variables(load_path)
+
+ rollout_params = {
+ 'exploit': False,
+ 'use_target_net': False,
+ 'use_demo_states': True,
+ 'compute_Q': False,
+ 'T': params['T'],
+ }
+
+ eval_params = {
+ 'exploit': True,
+ 'use_target_net': params['test_with_polyak'],
+ 'use_demo_states': False,
+ 'compute_Q': True,
+ 'T': params['T'],
+ }
+
+ for name in ['T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps']:
+ rollout_params[name] = params[name]
+ eval_params[name] = params[name]
+
+ eval_env = eval_env or env
+
+ rollout_worker = RolloutWorker(env, policy, dims, logger, monitor=True, **rollout_params)
+ evaluator = RolloutWorker(eval_env, policy, dims, logger, **eval_params)
+
+ n_cycles = params['n_cycles']
+ n_epochs = total_timesteps // n_cycles // rollout_worker.T // rollout_worker.rollout_batch_size
+
+ return train(
+ save_path=save_path, policy=policy, rollout_worker=rollout_worker,
+ evaluator=evaluator, n_epochs=n_epochs, n_test_rollouts=params['n_test_rollouts'],
+ n_cycles=params['n_cycles'], n_batches=params['n_batches'],
+ policy_save_interval=policy_save_interval, demo_file=demo_file)
+
+
+@click.command()
+@click.option('--env', type=str, default='FetchReach-v1', help='the name of the OpenAI Gym environment that you want to train on')
+@click.option('--total_timesteps', type=int, default=int(5e5), help='the number of timesteps to run')
+@click.option('--seed', type=int, default=0, help='the random seed used to seed both the environment and the training code')
+@click.option('--policy_save_interval', type=int, default=5, help='the interval with which policy pickles are saved. If set to 0, only the best and latest policy will be pickled.')
+@click.option('--replay_strategy', type=click.Choice(['future', 'none']), default='future', help='the HER replay strategy to be used. "future" uses HER, "none" disables HER.')
+@click.option('--clip_return', type=int, default=1, help='whether or not returns should be clipped')
+@click.option('--demo_file', type=str, default = 'PATH/TO/DEMO/DATA/FILE.npz', help='demo data file path')
+def main(**kwargs):
+ learn(**kwargs)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/baselines/baselines/her/her_sampler.py b/baselines/baselines/her/her_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..76f3c346ae34f93150926ba70d1a4987be484ddd
--- /dev/null
+++ b/baselines/baselines/her/her_sampler.py
@@ -0,0 +1,63 @@
+import numpy as np
+
+
+def make_sample_her_transitions(replay_strategy, replay_k, reward_fun):
+ """Creates a sample function that can be used for HER experience replay.
+
+ Args:
+ replay_strategy (in ['future', 'none']): the HER replay strategy; if set to 'none',
+ regular DDPG experience replay is used
+ replay_k (int): the ratio between HER replays and regular replays (e.g. k = 4 -> 4 times
+ as many HER replays as regular replays are used)
+ reward_fun (function): function to re-compute the reward with substituted goals
+ """
+ if replay_strategy == 'future':
+ future_p = 1 - (1. / (1 + replay_k))
+ else: # 'replay_strategy' == 'none'
+ future_p = 0
+
+ def _sample_her_transitions(episode_batch, batch_size_in_transitions):
+ """episode_batch is {key: array(buffer_size x T x dim_key)}
+ """
+ T = episode_batch['u'].shape[1]
+ rollout_batch_size = episode_batch['u'].shape[0]
+ batch_size = batch_size_in_transitions
+
+ # Select which episodes and time steps to use.
+ episode_idxs = np.random.randint(0, rollout_batch_size, batch_size)
+ t_samples = np.random.randint(T, size=batch_size)
+ transitions = {key: episode_batch[key][episode_idxs, t_samples].copy()
+ for key in episode_batch.keys()}
+
+ # Select future time indexes proportional with probability future_p. These
+ # will be used for HER replay by substituting in future goals.
+ her_indexes = np.where(np.random.uniform(size=batch_size) < future_p)
+ future_offset = np.random.uniform(size=batch_size) * (T - t_samples)
+ future_offset = future_offset.astype(int)
+ future_t = (t_samples + 1 + future_offset)[her_indexes]
+
+ # Replace goal with achieved goal but only for the previously-selected
+ # HER transitions (as defined by her_indexes). For the other transitions,
+ # keep the original goal.
+ future_ag = episode_batch['ag'][episode_idxs[her_indexes], future_t]
+ transitions['g'][her_indexes] = future_ag
+
+ # Reconstruct info dictionary for reward computation.
+ info = {}
+ for key, value in transitions.items():
+ if key.startswith('info_'):
+ info[key.replace('info_', '')] = value
+
+ # Re-compute reward since we may have substituted the goal.
+ reward_params = {k: transitions[k] for k in ['ag_2', 'g']}
+ reward_params['info'] = info
+ transitions['r'] = reward_fun(**reward_params)
+
+ transitions = {k: transitions[k].reshape(batch_size, *transitions[k].shape[1:])
+ for k in transitions.keys()}
+
+ assert(transitions['u'].shape[0] == batch_size_in_transitions)
+
+ return transitions
+
+ return _sample_her_transitions
diff --git a/baselines/baselines/her/normalizer.py b/baselines/baselines/her/normalizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d02967cd3fcae035f830ebaa1dec75efa9989220
--- /dev/null
+++ b/baselines/baselines/her/normalizer.py
@@ -0,0 +1,139 @@
+import threading
+
+import numpy as np
+from mpi4py import MPI
+import tensorflow as tf
+
+from baselines.her.util import reshape_for_broadcasting
+
+
+class Normalizer(tf.Module):
+ def __init__(self, size, eps=1e-2, default_clip_range=np.inf):
+ """A normalizer that ensures that observations are approximately distributed according to
+ a standard Normal distribution (i.e. have mean zero and variance one).
+
+ Args:
+ size (int): the size of the observation to be normalized
+ eps (float): a small constant that avoids underflows
+ default_clip_range (float): normalized observations are clipped to be in
+ [-default_clip_range, default_clip_range]
+ sess (object): the TensorFlow session to be used
+ """
+ self.size = size
+ self.eps = eps
+ self.default_clip_range = default_clip_range
+
+ self.local_sum = np.zeros(self.size, np.float32)
+ self.local_sumsq = np.zeros(self.size, np.float32)
+ self.local_count = np.zeros(1, np.float32)
+
+ self.sum_tf = tf.get_variable(
+ initializer=tf.zeros_initializer(), shape=self.local_sum.shape, name='sum',
+ trainable=False, dtype=tf.float32)
+ self.sumsq_tf = tf.get_variable(
+ initializer=tf.zeros_initializer(), shape=self.local_sumsq.shape, name='sumsq',
+ trainable=False, dtype=tf.float32)
+ self.count_tf = tf.get_variable(
+ initializer=tf.ones_initializer(), shape=self.local_count.shape, name='count',
+ trainable=False, dtype=tf.float32)
+ self.mean = tf.get_variable(
+ initializer=tf.zeros_initializer(), shape=(self.size,), name='mean',
+ trainable=False, dtype=tf.float32)
+ self.std = tf.get_variable(
+ initializer=tf.ones_initializer(), shape=(self.size,), name='std',
+ trainable=False, dtype=tf.float32)
+ self.count_pl = tf.placeholder(name='count_pl', shape=(1,), dtype=tf.float32)
+ self.sum_pl = tf.placeholder(name='sum_pl', shape=(self.size,), dtype=tf.float32)
+ self.sumsq_pl = tf.placeholder(name='sumsq_pl', shape=(self.size,), dtype=tf.float32)
+
+ self.update_op = tf.group(
+ self.count_tf.assign_add(self.count_pl),
+ self.sum_tf.assign_add(self.sum_pl),
+ self.sumsq_tf.assign_add(self.sumsq_pl)
+ )
+ self.recompute_op = tf.group(
+ tf.assign(self.mean, self.sum_tf / self.count_tf),
+ tf.assign(self.std, tf.sqrt(tf.maximum(
+ tf.square(self.eps),
+ self.sumsq_tf / self.count_tf - tf.square(self.sum_tf / self.count_tf)
+ ))),
+ )
+ self.lock = threading.Lock()
+
+ def update(self, v):
+ v = v.reshape(-1, self.size)
+
+ with self.lock:
+ self.local_sum += v.sum(axis=0)
+ self.local_sumsq += (np.square(v)).sum(axis=0)
+ self.local_count[0] += v.shape[0]
+
+ def normalize(self, v, clip_range=None):
+ if clip_range is None:
+ clip_range = self.default_clip_range
+ mean = reshape_for_broadcasting(self.mean, v)
+ std = reshape_for_broadcasting(self.std, v)
+ return tf.clip_by_value((v - mean) / std, -clip_range, clip_range)
+
+ def denormalize(self, v):
+ mean = reshape_for_broadcasting(self.mean, v)
+ std = reshape_for_broadcasting(self.std, v)
+ return mean + v * std
+
+ def _mpi_average(self, x):
+ buf = np.zeros_like(x)
+ MPI.COMM_WORLD.Allreduce(x, buf, op=MPI.SUM)
+ buf /= MPI.COMM_WORLD.Get_size()
+ return buf
+
+ def synchronize(self, local_sum, local_sumsq, local_count, root=None):
+ local_sum[...] = self._mpi_average(local_sum)
+ local_sumsq[...] = self._mpi_average(local_sumsq)
+ local_count[...] = self._mpi_average(local_count)
+ return local_sum, local_sumsq, local_count
+
+ def recompute_stats(self):
+ with self.lock:
+ # Copy over results.
+ local_count = self.local_count.copy()
+ local_sum = self.local_sum.copy()
+ local_sumsq = self.local_sumsq.copy()
+
+ # Reset.
+ self.local_count[...] = 0
+ self.local_sum[...] = 0
+ self.local_sumsq[...] = 0
+
+ # We perform the synchronization outside of the lock to keep the critical section as short
+ # as possible.
+ synced_sum, synced_sumsq, synced_count = self.synchronize(
+ local_sum=local_sum, local_sumsq=local_sumsq, local_count=local_count)
+
+ self.sess.run(self.update_op, feed_dict={
+ self.count_pl: synced_count,
+ self.sum_pl: synced_sum,
+ self.sumsq_pl: synced_sumsq,
+ })
+ self.sess.run(self.recompute_op)
+
+
+class IdentityNormalizer:
+ def __init__(self, size, std=1.):
+ self.size = size
+ self.mean = tf.zeros(self.size, tf.float32)
+ self.std = std * tf.ones(self.size, tf.float32)
+
+ def update(self, x):
+ pass
+
+ def normalize(self, x, clip_range=None):
+ return x / self.std
+
+ def denormalize(self, x):
+ return self.std * x
+
+ def synchronize(self):
+ pass
+
+ def recompute_stats(self):
+ pass
diff --git a/baselines/baselines/her/replay_buffer.py b/baselines/baselines/her/replay_buffer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0005523fd63a8f85acc50ab8a30b0c2b8253d1f
--- /dev/null
+++ b/baselines/baselines/her/replay_buffer.py
@@ -0,0 +1,108 @@
+import threading
+
+import numpy as np
+
+
+class ReplayBuffer:
+ def __init__(self, buffer_shapes, size_in_transitions, T, sample_transitions):
+ """Creates a replay buffer.
+
+ Args:
+ buffer_shapes (dict of ints): the shape for all buffers that are used in the replay
+ buffer
+ size_in_transitions (int): the size of the buffer, measured in transitions
+ T (int): the time horizon for episodes
+ sample_transitions (function): a function that samples from the replay buffer
+ """
+ self.buffer_shapes = buffer_shapes
+ self.size = size_in_transitions // T
+ self.T = T
+ self.sample_transitions = sample_transitions
+
+ # self.buffers is {key: array(size_in_episodes x T or T+1 x dim_key)}
+ self.buffers = {key: np.empty([self.size, *shape])
+ for key, shape in buffer_shapes.items()}
+
+ # memory management
+ self.current_size = 0
+ self.n_transitions_stored = 0
+
+ self.lock = threading.Lock()
+
+ @property
+ def full(self):
+ with self.lock:
+ return self.current_size == self.size
+
+ def sample(self, batch_size):
+ """Returns a dict {key: array(batch_size x shapes[key])}
+ """
+ buffers = {}
+
+ with self.lock:
+ assert self.current_size > 0
+ for key in self.buffers.keys():
+ buffers[key] = self.buffers[key][:self.current_size]
+
+ buffers['o_2'] = buffers['o'][:, 1:, :]
+ buffers['ag_2'] = buffers['ag'][:, 1:, :]
+
+ transitions = self.sample_transitions(buffers, batch_size)
+
+ for key in (['r', 'o_2', 'ag_2'] + list(self.buffers.keys())):
+ assert key in transitions, "key %s missing from transitions" % key
+
+ return transitions
+
+ def store_episode(self, episode_batch):
+ """episode_batch: array(batch_size x (T or T+1) x dim_key)
+ """
+ batch_sizes = [len(episode_batch[key]) for key in episode_batch.keys()]
+ assert np.all(np.array(batch_sizes) == batch_sizes[0])
+ batch_size = batch_sizes[0]
+
+ with self.lock:
+ idxs = self._get_storage_idx(batch_size)
+
+ # load inputs into buffers
+ for key in self.buffers.keys():
+ self.buffers[key][idxs] = episode_batch[key]
+
+ self.n_transitions_stored += batch_size * self.T
+
+ def get_current_episode_size(self):
+ with self.lock:
+ return self.current_size
+
+ def get_current_size(self):
+ with self.lock:
+ return self.current_size * self.T
+
+ def get_transitions_stored(self):
+ with self.lock:
+ return self.n_transitions_stored
+
+ def clear_buffer(self):
+ with self.lock:
+ self.current_size = 0
+
+ def _get_storage_idx(self, inc=None):
+ inc = inc or 1 # size increment
+ assert inc <= self.size, "Batch committed to replay is too large!"
+ # go consecutively until you hit the end, and then go randomly.
+ if self.current_size+inc <= self.size:
+ idx = np.arange(self.current_size, self.current_size+inc)
+ elif self.current_size < self.size:
+ overflow = inc - (self.size - self.current_size)
+ idx_a = np.arange(self.current_size, self.size)
+ idx_b = np.random.randint(0, self.current_size, overflow)
+ idx = np.concatenate([idx_a, idx_b])
+ else:
+ idx = np.random.randint(0, self.size, inc)
+
+ # update replay size
+ self.current_size = min(self.size, self.current_size+inc)
+
+ if inc == 1:
+ idx = idx[0]
+ return idx
diff --git a/baselines/baselines/her/rollout.py b/baselines/baselines/her/rollout.py
new file mode 100644
index 0000000000000000000000000000000000000000..3235ab78e3cbb70a9e6a0377c9b5c41952506ee7
--- /dev/null
+++ b/baselines/baselines/her/rollout.py
@@ -0,0 +1,170 @@
+from collections import deque
+
+import numpy as np
+import pickle
+
+from baselines.her.util import convert_episode_to_batch_major, store_args
+
+
+class RolloutWorker:
+
+ @store_args
+ def __init__(self, venv, policy, dims, logger, T, rollout_batch_size=1,
+ exploit=False, use_target_net=False, compute_Q=False, noise_eps=0,
+ random_eps=0, history_len=100, render=False, monitor=False, **kwargs):
+ """Rollout worker generates experience by interacting with one or many environments.
+
+ Args:
+ make_env (function): a factory function that creates a new instance of the environment
+ when called
+ policy (object): the policy that is used to act
+ dims (dict of ints): the dimensions for observations (o), goals (g), and actions (u)
+ logger (object): the logger that is used by the rollout worker
+ rollout_batch_size (int): the number of parallel rollouts that should be used
+ exploit (boolean): whether or not to exploit, i.e. to act optimally according to the
+ current policy without any exploration
+ use_target_net (boolean): whether or not to use the target net for rollouts
+ compute_Q (boolean): whether or not to compute the Q values alongside the actions
+ noise_eps (float): scale of the additive Gaussian noise
+ random_eps (float): probability of selecting a completely random action
+ history_len (int): length of history for statistics smoothing
+ render (boolean): whether or not to render the rollouts
+ """
+
+ assert self.T > 0
+
+ self.info_keys = [key.replace('info_', '') for key in dims.keys() if key.startswith('info_')]
+
+ self.success_history = deque(maxlen=history_len)
+ self.Q_history = deque(maxlen=history_len)
+
+ self.n_episodes = 0
+ self.reset_all_rollouts()
+ self.clear_history()
+
+ def reset_all_rollouts(self):
+ self.obs_dict = self.venv.reset()
+ self.initial_o = self.obs_dict['observation']
+ self.initial_ag = self.obs_dict['achieved_goal']
+ self.g = self.obs_dict['desired_goal']
+
+ def generate_rollouts(self):
+ """Performs `rollout_batch_size` rollouts in parallel for time horizon `T` with the current
+ policy acting on it accordingly.
+ """
+ self.reset_all_rollouts()
+
+ # compute observations
+ o = np.empty((self.rollout_batch_size, self.dims['o']), np.float32) # observations
+ ag = np.empty((self.rollout_batch_size, self.dims['g']), np.float32) # achieved goals
+ o[:] = self.initial_o
+ ag[:] = self.initial_ag
+
+ # generate episodes
+ obs, achieved_goals, acts, goals, successes = [], [], [], [], []
+ dones = []
+ info_values = [np.empty((self.T - 1, self.rollout_batch_size, self.dims['info_' + key]), np.float32) for key in self.info_keys]
+ Qs = []
+ for t in range(self.T):
+ policy_output = self.policy.get_actions(
+ o, ag, self.g,
+ compute_Q=self.compute_Q,
+ noise_eps=self.noise_eps if not self.exploit else 0.,
+ random_eps=self.random_eps if not self.exploit else 0.,
+ use_target_net=self.use_target_net)
+
+ if self.compute_Q:
+ u, Q = policy_output
+ Qs.append(Q)
+ else:
+ u = policy_output
+
+ if u.ndim == 1:
+ # The non-batched case should still have a reasonable shape.
+ u = u.reshape(1, -1)
+
+ o_new = np.empty((self.rollout_batch_size, self.dims['o']))
+ ag_new = np.empty((self.rollout_batch_size, self.dims['g']))
+ success = np.zeros(self.rollout_batch_size)
+ # compute new states and observations
+ obs_dict_new, _, done, info = self.venv.step(u)
+ o_new = obs_dict_new['observation']
+ ag_new = obs_dict_new['achieved_goal']
+ success = np.array([i.get('is_success', 0.0) for i in info])
+
+ if any(done):
+ # here we assume all environments are done is ~same number of steps, so we terminate rollouts whenever any of the envs returns done
+ # trick with using vecenvs is not to add the obs from the environments that are "done", because those are already observations
+ # after a reset
+ break
+
+ for i, info_dict in enumerate(info):
+ for idx, key in enumerate(self.info_keys):
+ info_values[idx][t, i] = info[i][key]
+
+ if np.isnan(o_new).any():
+ self.logger.warn('NaN caught during rollout generation. Trying again...')
+ self.reset_all_rollouts()
+ return self.generate_rollouts()
+
+ dones.append(done)
+ obs.append(o.copy())
+ achieved_goals.append(ag.copy())
+ successes.append(success.copy())
+ acts.append(u.copy())
+ goals.append(self.g.copy())
+ o[...] = o_new
+ ag[...] = ag_new
+ obs.append(o.copy())
+ achieved_goals.append(ag.copy())
+
+ episode = dict(o=obs,
+ u=acts,
+ g=goals,
+ ag=achieved_goals)
+ for key, value in zip(self.info_keys, info_values):
+ episode['info_{}'.format(key)] = value
+
+ # stats
+ successful = np.array(successes)[-1, :]
+ assert successful.shape == (self.rollout_batch_size,)
+ success_rate = np.mean(successful)
+ self.success_history.append(success_rate)
+ if self.compute_Q:
+ self.Q_history.append(np.mean(Qs))
+ self.n_episodes += self.rollout_batch_size
+
+ return convert_episode_to_batch_major(episode)
+
+ def clear_history(self):
+ """Clears all histories that are used for statistics
+ """
+ self.success_history.clear()
+ self.Q_history.clear()
+
+ def current_success_rate(self):
+ return np.mean(self.success_history)
+
+ def current_mean_Q(self):
+ return np.mean(self.Q_history)
+
+ def save_policy(self, path):
+ """Pickles the current policy for later inspection.
+ """
+ with open(path, 'wb') as f:
+ pickle.dump(self.policy, f)
+
+ def logs(self, prefix='worker'):
+ """Generates a dictionary that contains all collected statistics.
+ """
+ logs = []
+ logs += [('success_rate', np.mean(self.success_history))]
+ if self.compute_Q:
+ logs += [('mean_Q', np.mean(self.Q_history))]
+ logs += [('episode', self.n_episodes)]
+
+ if prefix != '' and not prefix.endswith('/'):
+ return [(prefix + '/' + key, val) for key, val in logs]
+ else:
+ return logs
+
diff --git a/baselines/baselines/her/util.py b/baselines/baselines/her/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a4947b66c9a6513ca2dcba74fa177f3ab96fc19
--- /dev/null
+++ b/baselines/baselines/her/util.py
@@ -0,0 +1,137 @@
+import os
+import subprocess
+import sys
+import importlib
+import inspect
+import functools
+
+import tensorflow as tf
+import numpy as np
+
+from baselines.common import tf_util as U
+
+
+def store_args(method):
+ """Stores provided method args as instance attributes.
+ """
+ argspec = inspect.getfullargspec(method)
+ defaults = {}
+ if argspec.defaults is not None:
+ defaults = dict(
+ zip(argspec.args[-len(argspec.defaults):], argspec.defaults))
+ if argspec.kwonlydefaults is not None:
+ defaults.update(argspec.kwonlydefaults)
+ arg_names = argspec.args[1:]
+
+ @functools.wraps(method)
+ def wrapper(*positional_args, **keyword_args):
+ self = positional_args[0]
+ # Get default arg values
+ args = defaults.copy()
+ # Add provided arg values
+ for name, value in zip(arg_names, positional_args[1:]):
+ args[name] = value
+ args.update(keyword_args)
+ self.__dict__.update(args)
+ return method(*positional_args, **keyword_args)
+
+ return wrapper
+
+
+def import_function(spec):
+ """Import a function identified by a string like "pkg.module:fn_name".
+ """
+ mod_name, fn_name = spec.split(':')
+ module = importlib.import_module(mod_name)
+ fn = getattr(module, fn_name)
+ return fn
+
+
+def flatten_grads(var_list, grads):
+ """Flattens a variables and their gradients.
+ """
+ return tf.concat([tf.reshape(grad, [U.numel(v)])
+ for (v, grad) in zip(var_list, grads)], 0)
+
+
+def nn(input_shape, layers_sizes, name="", output_activation=None):
+ """Creates a simple neural network
+ """
+ print('input shape is {}'.format(input_shape))
+ x_input = tf.keras.Input(shape=input_shape)
+ h = x_input
+ for i, size in enumerate(layers_sizes):
+ activation = 'relu' if i < len(layers_sizes) - 1 else output_activation
+ h = tf.keras.layers.Dense(
+ units=size, kernel_initializer='glorot_uniform', activation=activation,
+ name=name + '_' + str(i))(h)
+ network = tf.keras.Model(inputs=[x_input], outputs=[h])
+ return network
+
+
+def install_mpi_excepthook():
+ import sys
+ from mpi4py import MPI
+ old_hook = sys.excepthook
+
+ def new_hook(a, b, c):
+ old_hook(a, b, c)
+ sys.stdout.flush()
+ sys.stderr.flush()
+ MPI.COMM_WORLD.Abort()
+ sys.excepthook = new_hook
+
+
+def mpi_fork(n, extra_mpi_args=[]):
+ """Re-launches the current script with workers
+ Returns "parent" for original parent, "child" for MPI children
+ """
+ if n <= 1:
+ return "child"
+ if os.getenv("IN_MPI") is None:
+ env = os.environ.copy()
+ env.update(
+ MKL_NUM_THREADS="1",
+ OMP_NUM_THREADS="1",
+ IN_MPI="1"
+ )
+ # "-bind-to core" is crucial for good performance
+ args = ["mpirun", "-np", str(n)] + \
+ extra_mpi_args + \
+ [sys.executable]
+
+ args += sys.argv
+ subprocess.check_call(args, env=env)
+ return "parent"
+ else:
+ install_mpi_excepthook()
+ return "child"
+
+
+def convert_episode_to_batch_major(episode):
+ """Converts an episode to have the batch dimension in the major (first)
+ dimension.
+ """
+ episode_batch = {}
+ for key in episode.keys():
+ val = np.array(episode[key]).copy()
+ # make inputs batch-major instead of time-major
+ episode_batch[key] = val.swapaxes(0, 1)
+
+ return episode_batch
+
+
+def transitions_in_episode_batch(episode_batch):
+ """Number of transitions in a given episode batch.
+ """
+ shape = episode_batch['u'].shape
+ return shape[0] * shape[1]
+
+
+def reshape_for_broadcasting(source, target):
+ """Reshapes a tensor (source) to have the correct shape and dtype of the target
+ before broadcasting it with MPI.
+ """
+ dim = len(target.get_shape())
+ shape = ([1] * (dim - 1)) + [-1]
+ return tf.reshape(tf.cast(source, target.dtype), shape)
diff --git a/baselines/baselines/logger.py b/baselines/baselines/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..e74f99bc0beabaa1cff5c464b9fa96c3d996afd1
--- /dev/null
+++ b/baselines/baselines/logger.py
@@ -0,0 +1,501 @@
+import os
+import sys
+import shutil
+import os.path as osp
+import json
+import time
+import datetime
+import tempfile
+from collections import defaultdict
+from contextlib import contextmanager
+
+DEBUG = 10
+INFO = 20
+WARN = 30
+ERROR = 40
+
+DISABLED = 50
+
+class KVWriter(object):
+ def writekvs(self, kvs):
+ raise NotImplementedError
+
+class SeqWriter(object):
+ def writeseq(self, seq):
+ raise NotImplementedError
+
+class HumanOutputFormat(KVWriter, SeqWriter):
+ def __init__(self, filename_or_file):
+ if isinstance(filename_or_file, str):
+ self.file = open(filename_or_file, 'wt')
+ self.own_file = True
+ else:
+ assert hasattr(filename_or_file, 'read'), 'expected file or str, got %s'%filename_or_file
+ self.file = filename_or_file
+ self.own_file = False
+
+ def writekvs(self, kvs):
+ # Create strings for printing
+ key2str = {}
+ for (key, val) in sorted(kvs.items()):
+ if isinstance(val, float):
+ valstr = '%-8.3g' % (val,)
+ else:
+ valstr = str(val)
+ key2str[self._truncate(key)] = self._truncate(valstr)
+
+ # Find max widths
+ if len(key2str) == 0:
+ print('WARNING: tried to write empty key-value dict')
+ return
+ else:
+ keywidth = max(map(len, key2str.keys()))
+ valwidth = max(map(len, key2str.values()))
+
+ # Write out the data
+ dashes = '-' * (keywidth + valwidth + 7)
+ lines = [dashes]
+ for (key, val) in sorted(key2str.items(), key=lambda kv: kv[0].lower()):
+ lines.append('| %s%s | %s%s |' % (
+ key,
+ ' ' * (keywidth - len(key)),
+ val,
+ ' ' * (valwidth - len(val)),
+ ))
+ lines.append(dashes)
+ self.file.write('\n'.join(lines) + '\n')
+
+ # Flush the output to the file
+ self.file.flush()
+
+ def _truncate(self, s):
+ maxlen = 30
+ return s[:maxlen-3] + '...' if len(s) > maxlen else s
+
+ def writeseq(self, seq):
+ seq = list(seq)
+ for (i, elem) in enumerate(seq):
+ self.file.write(elem)
+ if i < len(seq) - 1: # add space unless this is the last one
+ self.file.write(' ')
+ self.file.write('\n')
+ self.file.flush()
+
+ def close(self):
+ if self.own_file:
+ self.file.close()
+
+class JSONOutputFormat(KVWriter):
+ def __init__(self, filename):
+ self.file = open(filename, 'wt')
+
+ def writekvs(self, kvs):
+ for k, v in sorted(kvs.items()):
+ if hasattr(v, 'dtype'):
+ v = v.tolist()
+ kvs[k] = float(v)
+ self.file.write(json.dumps(kvs) + '\n')
+ self.file.flush()
+
+ def close(self):
+ self.file.close()
+
+class CSVOutputFormat(KVWriter):
+ def __init__(self, filename):
+ self.file = open(filename, 'w+t')
+ self.keys = []
+ self.sep = ','
+
+ def writekvs(self, kvs):
+ # Add our current row to the history
+ extra_keys = list(kvs.keys() - self.keys)
+ extra_keys.sort()
+ if extra_keys:
+ self.keys.extend(extra_keys)
+ self.file.seek(0)
+ lines = self.file.readlines()
+ self.file.seek(0)
+ for (i, k) in enumerate(self.keys):
+ if i > 0:
+ self.file.write(',')
+ self.file.write(k)
+ self.file.write('\n')
+ for line in lines[1:]:
+ self.file.write(line[:-1])
+ self.file.write(self.sep * len(extra_keys))
+ self.file.write('\n')
+ for (i, k) in enumerate(self.keys):
+ if i > 0:
+ self.file.write(',')
+ v = kvs.get(k)
+ if v is not None:
+ self.file.write(str(v))
+ self.file.write('\n')
+ self.file.flush()
+
+ def close(self):
+ self.file.close()
+
+
+class TensorBoardOutputFormat(KVWriter):
+ """
+ Dumps key/value pairs into TensorBoard's numeric format.
+ """
+ def __init__(self, dir):
+ os.makedirs(dir, exist_ok=True)
+ self.dir = dir
+ self.step = 1
+ prefix = 'events'
+ path = osp.join(osp.abspath(dir), prefix)
+ import tensorflow as tf
+ from tensorflow.python import pywrap_tensorflow
+ from tensorflow.core.util import event_pb2
+ from tensorflow.python.util import compat
+ self.tf = tf
+ self.event_pb2 = event_pb2
+ self.pywrap_tensorflow = pywrap_tensorflow
+ self.writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(path))
+
+ def writekvs(self, kvs):
+ def summary_val(k, v):
+ kwargs = {'tag': k, 'simple_value': float(v)}
+ return self.tf.Summary.Value(**kwargs)
+ summary = self.tf.Summary(value=[summary_val(k, v) for k, v in kvs.items()])
+ event = self.event_pb2.Event(wall_time=time.time(), summary=summary)
+ event.step = self.step # is there any reason why you'd want to specify the step?
+ self.writer.WriteEvent(event)
+ self.writer.Flush()
+ self.step += 1
+
+ def close(self):
+ if self.writer:
+ self.writer.Close()
+ self.writer = None
+
+def make_output_format(format, ev_dir, log_suffix=''):
+ os.makedirs(ev_dir, exist_ok=True)
+ if format == 'stdout':
+ return HumanOutputFormat(sys.stdout)
+ elif format == 'log':
+ return HumanOutputFormat(osp.join(ev_dir, 'log%s.txt' % log_suffix))
+ elif format == 'json':
+ return JSONOutputFormat(osp.join(ev_dir, 'progress%s.json' % log_suffix))
+ elif format == 'csv':
+ return CSVOutputFormat(osp.join(ev_dir, 'progress%s.csv' % log_suffix))
+ elif format == 'tensorboard':
+ return TensorBoardOutputFormat(osp.join(ev_dir, 'tb%s' % log_suffix))
+ else:
+ raise ValueError('Unknown format specified: %s' % (format,))
+
+# ================================================================
+# API
+# ================================================================
+
+def logkv(key, val):
+ """
+ Log a value of some diagnostic
+ Call this once for each diagnostic quantity, each iteration
+ If called many times, last value will be used.
+ """
+ get_current().logkv(key, val)
+
+def logkv_mean(key, val):
+ """
+ The same as logkv(), but if called many times, values averaged.
+ """
+ get_current().logkv_mean(key, val)
+
+def logkvs(d):
+ """
+ Log a dictionary of key-value pairs
+ """
+ for (k, v) in d.items():
+ logkv(k, v)
+
+def dumpkvs():
+ """
+ Write all of the diagnostics from the current iteration
+ """
+ return get_current().dumpkvs()
+
+def getkvs():
+ return get_current().name2val
+
+
+def log(*args, level=INFO):
+ """
+ Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).
+ """
+ get_current().log(*args, level=level)
+
+def debug(*args):
+ log(*args, level=DEBUG)
+
+def info(*args):
+ log(*args, level=INFO)
+
+def warn(*args):
+ log(*args, level=WARN)
+
+def error(*args):
+ log(*args, level=ERROR)
+
+
+def set_level(level):
+ """
+ Set logging threshold on current logger.
+ """
+ get_current().set_level(level)
+
+def set_comm(comm):
+ get_current().set_comm(comm)
+
+def get_dir():
+ """
+ Get directory that log files are being written to.
+ will be None if there is no output directory (i.e., if you didn't call start)
+ """
+ return get_current().get_dir()
+
+record_tabular = logkv
+dump_tabular = dumpkvs
+
+@contextmanager
+def profile_kv(scopename):
+ logkey = 'wait_' + scopename
+ tstart = time.time()
+ try:
+ yield
+ finally:
+ get_current().name2val[logkey] += time.time() - tstart
+
+def profile(n):
+ """
+ Usage:
+ @profile("my_func")
+ def my_func(): code
+ """
+ def decorator_with_name(func):
+ def func_wrapper(*args, **kwargs):
+ with profile_kv(n):
+ return func(*args, **kwargs)
+ return func_wrapper
+ return decorator_with_name
+
+
+# ================================================================
+# Backend
+# ================================================================
+
+def get_current():
+ if Logger.CURRENT is None:
+ _configure_default_logger()
+
+ return Logger.CURRENT
+
+
+class Logger(object):
+ DEFAULT = None # A logger with no output files. (See right below class definition)
+ # So that you can still log to the terminal without setting up any output files
+ CURRENT = None # Current logger being used by the free functions above
+
+ def __init__(self, dir, output_formats, comm=None):
+ self.name2val = defaultdict(float) # values this iteration
+ self.name2cnt = defaultdict(int)
+ self.level = INFO
+ self.dir = dir
+ self.output_formats = output_formats
+ self.comm = comm
+
+ # Logging API, forwarded
+ # ----------------------------------------
+ def logkv(self, key, val):
+ self.name2val[key] = val
+
+ def logkv_mean(self, key, val):
+ oldval, cnt = self.name2val[key], self.name2cnt[key]
+ self.name2val[key] = oldval*cnt/(cnt+1) + val/(cnt+1)
+ self.name2cnt[key] = cnt + 1
+
+ def dumpkvs(self):
+ if self.comm is None:
+ d = self.name2val
+ else:
+ from baselines.common import mpi_util
+ d = mpi_util.mpi_weighted_mean(self.comm,
+ {name : (val, self.name2cnt.get(name, 1))
+ for (name, val) in self.name2val.items()})
+ if self.comm.rank != 0:
+ d['dummy'] = 1 # so we don't get a warning about empty dict
+ out = d.copy() # Return the dict for unit testing purposes
+ for fmt in self.output_formats:
+ if isinstance(fmt, KVWriter):
+ fmt.writekvs(d)
+ self.name2val.clear()
+ self.name2cnt.clear()
+ return out
+
+ def log(self, *args, level=INFO):
+ if self.level <= level:
+ self._do_log(args)
+
+ # Configuration
+ # ----------------------------------------
+ def set_level(self, level):
+ self.level = level
+
+ def set_comm(self, comm):
+ self.comm = comm
+
+ def get_dir(self):
+ return self.dir
+
+ def close(self):
+ for fmt in self.output_formats:
+ fmt.close()
+
+ # Misc
+ # ----------------------------------------
+ def _do_log(self, args):
+ for fmt in self.output_formats:
+ if isinstance(fmt, SeqWriter):
+ fmt.writeseq(map(str, args))
+
+def get_rank_without_mpi_import():
+ # check environment variables here instead of importing mpi4py
+ # to avoid calling MPI_Init() when this module is imported
+ for varname in ['PMI_RANK', 'OMPI_COMM_WORLD_RANK']:
+ if varname in os.environ:
+ return int(os.environ[varname])
+ return 0
+
+def configure(dir=None, format_strs=None, comm=None, log_suffix=''):
+ """
+ If comm is provided, average all numerical stats across that comm
+ """
+ if dir is None:
+ dir = os.getenv('OPENAI_LOGDIR')
+ if dir is None:
+ dir = osp.join(tempfile.gettempdir(),
+ datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"))
+ assert isinstance(dir, str)
+ dir = os.path.expanduser(dir)
+ os.makedirs(os.path.expanduser(dir), exist_ok=True)
+
+ rank = get_rank_without_mpi_import()
+ if rank > 0:
+ log_suffix = "-rank%03i" % rank
+
+ if format_strs is None:
+ if rank == 0:
+ format_strs = os.getenv('OPENAI_LOG_FORMAT', 'stdout,log,csv').split(',')
+ else:
+ format_strs = os.getenv('OPENAI_LOG_FORMAT_MPI', 'log').split(',')
+ format_strs = filter(None, format_strs)
+ output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs]
+
+ Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm)
+ log('Logging to %s'%dir)
+
+def _configure_default_logger():
+ configure()
+ Logger.DEFAULT = Logger.CURRENT
+
+def reset():
+ if Logger.CURRENT is not Logger.DEFAULT:
+ Logger.CURRENT.close()
+ Logger.CURRENT = Logger.DEFAULT
+ log('Reset logger')
+
+@contextmanager
+def scoped_configure(dir=None, format_strs=None, comm=None):
+ prevlogger = Logger.CURRENT
+ configure(dir=dir, format_strs=format_strs, comm=comm)
+ try:
+ yield
+ finally:
+ Logger.CURRENT.close()
+ Logger.CURRENT = prevlogger
+
+# ================================================================
+
+def _demo():
+ info("hi")
+ debug("shouldn't appear")
+ set_level(DEBUG)
+ debug("should appear")
+ dir = "/tmp/testlogging"
+ if os.path.exists(dir):
+ shutil.rmtree(dir)
+ configure(dir=dir)
+ logkv("a", 3)
+ logkv("b", 2.5)
+ dumpkvs()
+ logkv("b", -2.5)
+ logkv("a", 5.5)
+ dumpkvs()
+ info("^^^ should see a = 5.5")
+ logkv_mean("b", -22.5)
+ logkv_mean("b", -44.4)
+ logkv("a", 5.5)
+ dumpkvs()
+ info("^^^ should see b = -33.3")
+
+ logkv("b", -2.5)
+ dumpkvs()
+
+ logkv("a", "longasslongasslongasslongasslongasslongassvalue")
+ dumpkvs()
+
+
+# ================================================================
+# Readers
+# ================================================================
+
+def read_json(fname):
+ import pandas
+ ds = []
+ with open(fname, 'rt') as fh:
+ for line in fh:
+ ds.append(json.loads(line))
+ return pandas.DataFrame(ds)
+
+def read_csv(fname):
+ import pandas
+ return pandas.read_csv(fname, index_col=None, comment='#')
+
+def read_tb(path):
+ """
+ path : a tensorboard file OR a directory, where we will find all TB files
+ of the form events.*
+ """
+ import pandas
+ import numpy as np
+ from glob import glob
+ import tensorflow as tf
+ if osp.isdir(path):
+ fnames = glob(osp.join(path, "events.*"))
+ elif osp.basename(path).startswith("events."):
+ fnames = [path]
+ else:
+ raise NotImplementedError("Expected tensorboard file or directory containing them. Got %s"%path)
+ tag2pairs = defaultdict(list)
+ maxstep = 0
+ for fname in fnames:
+ for summary in tf.train.summary_iterator(fname):
+ if summary.step > 0:
+ for v in summary.summary.value:
+ pair = (summary.step, v.simple_value)
+ tag2pairs[v.tag].append(pair)
+ maxstep = max(summary.step, maxstep)
+ data = np.empty((maxstep, len(tag2pairs)))
+ data[:] = np.nan
+ tags = sorted(tag2pairs.keys())
+ for (colidx,tag) in enumerate(tags):
+ pairs = tag2pairs[tag]
+ for (step, value) in pairs:
+ data[step-1, colidx] = value
+ return pandas.DataFrame(data, columns=tags)
+
+if __name__ == "__main__":
+ _demo()
diff --git a/baselines/baselines/ppo2/README.md b/baselines/baselines/ppo2/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4d431bca967caffedb5ea76ef38af4b9d99571d8
--- /dev/null
+++ b/baselines/baselines/ppo2/README.md
@@ -0,0 +1,8 @@
+# PPO2
+
+- Original paper: https://arxiv.org/abs/1707.06347
+- Baselines blog post: https://blog.openai.com/openai-baselines-ppo/
+
+- `python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
+- `python -m baselines.run --alg=ppo2 --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M frames on a Mujoco Ant environment.
+- also refer to the repo-wide [README.md](../../README.md#training-models)
diff --git a/baselines/baselines/ppo2/__init__.py b/baselines/baselines/ppo2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/baselines/baselines/ppo2/defaults.py b/baselines/baselines/ppo2/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f3f96e976e3f02f5819476f9e81896d5b8e8898
--- /dev/null
+++ b/baselines/baselines/ppo2/defaults.py
@@ -0,0 +1,25 @@
+def mujoco():
+ return dict(
+ nsteps=2048,
+ nminibatches=32,
+ lam=0.95,
+ gamma=0.99,
+ noptepochs=10,
+ log_interval=1,
+ ent_coef=0.0,
+ lr=lambda f: 3e-4 * f,
+ cliprange=0.2,
+ value_network='copy'
+ )
+
+def atari():
+ return dict(
+ nsteps=128, nminibatches=4,
+ lam=0.95, gamma=0.99, noptepochs=4, log_interval=1,
+ ent_coef=.01,
+ lr=lambda f : f * 2.5e-4,
+ cliprange=0.1,
+ )
+
+def retro():
+ return atari()
diff --git a/baselines/baselines/ppo2/model.py b/baselines/baselines/ppo2/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..f83d88cc98752116fdf2b707f38332ce3ce741dd
--- /dev/null
+++ b/baselines/baselines/ppo2/model.py
@@ -0,0 +1,90 @@
+import tensorflow as tf
+from baselines.common.policies import PolicyWithValue
+
+try:
+ from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
+ from mpi4py import MPI
+ from baselines.common.mpi_util import sync_from_root
+except ImportError:
+ MPI = None
+
+class Model(tf.Module):
+ """
+ We use this object to :
+ __init__:
+ - Creates the step_model
+ - Creates the train_model
+
+ train():
+ - Make the training part (feedforward and retropropagation of gradients)
+
+ save/load():
+ - Save load the model
+ """
+ def __init__(self, *, ac_space, policy_network, value_network=None, ent_coef, vf_coef, max_grad_norm):
+ super(Model, self).__init__(name='PPO2Model')
+ self.train_model = PolicyWithValue(ac_space, policy_network, value_network, estimate_q=False)
+ if MPI is not None:
+ self.optimizer = MpiAdamOptimizer(MPI.COMM_WORLD, self.train_model.trainable_variables)
+ else:
+ self.optimizer = tf.keras.optimizers.Adam()
+ self.ent_coef = ent_coef
+ self.vf_coef = vf_coef
+ self.max_grad_norm = max_grad_norm
+ self.step = self.train_model.step
+ self.value = self.train_model.value
+ self.initial_state = self.train_model.initial_state
+ self.loss_names = ['policy_loss', 'value_loss', 'policy_entropy', 'approxkl', 'clipfrac']
+ if MPI is not None:
+ sync_from_root(self.variables)
+
+ def train(self, lr, cliprange, obs, returns, masks, actions, values, neglogpac_old, states=None):
+ grads, pg_loss, vf_loss, entropy, approxkl, clipfrac = self.get_grad(
+ cliprange, obs, returns, masks, actions, values, neglogpac_old)
+ if MPI is not None:
+ self.optimizer.apply_gradients(grads, lr)
+ else:
+ self.optimizer.learning_rate = lr
+ grads_and_vars = zip(grads, self.train_model.trainable_variables)
+ self.optimizer.apply_gradients(grads_and_vars)
+
+ return pg_loss, vf_loss, entropy, approxkl, clipfrac
+
+
+ @tf.function
+ def get_grad(self, cliprange, obs, returns, masks, actions, values, neglogpac_old):
+ # Here we calculate advantage A(s,a) = R + yV(s') - V(s)
+ # Returns = R + yV(s')
+ advs = returns - values
+
+ # Normalize the advantages
+ advs = (advs - tf.reduce_mean(advs)) / (tf.keras.backend.std(advs) + 1e-8)
+
+ with tf.GradientTape() as tape:
+ policy_latent = self.train_model.policy_network(obs)
+ pd, _ = self.train_model.pdtype.pdfromlatent(policy_latent)
+ neglogpac = pd.neglogp(actions)
+ entropy = tf.reduce_mean(pd.entropy())
+ vpred = self.train_model.value(obs)
+ vpredclipped = values + tf.clip_by_value(vpred - values, -cliprange, cliprange)
+ vf_losses1 = tf.square(vpred - returns)
+ vf_losses2 = tf.square(vpredclipped - returns)
+ vf_loss = .5 * tf.reduce_mean(tf.maximum(vf_losses1, vf_losses2))
+
+ ratio = tf.exp(neglogpac_old - neglogpac)
+ pg_losses1 = -advs * ratio
+ pg_losses2 = -advs * tf.clip_by_value(ratio, 1-cliprange, 1+cliprange)
+ pg_loss = tf.reduce_mean(tf.maximum(pg_losses1, pg_losses2))
+
+ approxkl = .5 * tf.reduce_mean(tf.square(neglogpac - neglogpac_old))
+ clipfrac = tf.reduce_mean(tf.cast(tf.greater(tf.abs(ratio - 1.0), cliprange), tf.float32))
+
+ loss = pg_loss - entropy * self.ent_coef + vf_loss * self.vf_coef
+
+ var_list = self.train_model.trainable_variables
+ grads = tape.gradient(loss, var_list)
+ if self.max_grad_norm is not None:
+ grads, _ = tf.clip_by_global_norm(grads, self.max_grad_norm)
+ if MPI is not None:
+ grads = tf.concat([tf.reshape(g, (-1,)) for g in grads], axis=0)
+ return grads, pg_loss, vf_loss, entropy, approxkl, clipfrac
\ No newline at end of file
diff --git a/baselines/baselines/ppo2/ppo2.py b/baselines/baselines/ppo2/ppo2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec127adf7a6f3edfc08f7119d3a3544716b92498
--- /dev/null
+++ b/baselines/baselines/ppo2/ppo2.py
@@ -0,0 +1,200 @@
+import time
+import numpy as np
+import tensorflow as tf
+import os.path as osp
+from baselines import logger
+from collections import deque
+from baselines.common import explained_variance, set_global_seeds
+from baselines.common.models import get_network_builder
+try:
+ from mpi4py import MPI
+except ImportError:
+ MPI = None
+from baselines.ppo2.runner import Runner
+
+def constfn(val):
+ def f(_):
+ return val
+ return f
+
+def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
+ vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95,
+ log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
+ save_interval=0, load_path=None, model_fn=None, **network_kwargs):
+ '''
+ Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
+
+ Parameters:
+ ----------
+
+ network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
+ specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
+ tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
+ neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
+ See common/models.py/lstm for more details on using recurrent nets in policies
+
+ env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
+ The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
+
+
+ nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
+ nenv is number of environment copies simulated in parallel)
+
+ total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
+
+ ent_coef: float policy entropy coefficient in the optimization objective
+
+ lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
+ training and 0 is the end of the training.
+
+ vf_coef: float value function loss coefficient in the optimization objective
+
+ max_grad_norm: float or None gradient norm clipping coefficient
+
+ gamma: float discounting factor
+
+ lam: float advantage estimation discounting factor (lambda in the paper)
+
+ log_interval: int number of timesteps between logging events
+
+ nminibatches: int number of training minibatches per update. For recurrent policies,
+ should be smaller or equal than number of environments run in parallel.
+
+ noptepochs: int number of training epochs per update
+
+ cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
+ and 0 is the end of the training
+
+ save_interval: int number of timesteps between saving events
+
+ load_path: str path to load the model from
+
+ **network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
+ For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
+
+
+
+ '''
+
+ set_global_seeds(seed)
+
+ if isinstance(lr, float): lr = constfn(lr)
+ else: assert callable(lr)
+ if isinstance(cliprange, float): cliprange = constfn(cliprange)
+ else: assert callable(cliprange)
+ total_timesteps = int(total_timesteps)
+
+ # Get the nb of env
+ nenvs = env.num_envs
+
+ # Get state_space and action_space
+ ob_space = env.observation_space
+ ac_space = env.action_space
+
+ if isinstance(network, str):
+ network_type = network
+ policy_network_fn = get_network_builder(network_type)(**network_kwargs)
+ network = policy_network_fn(ob_space.shape)
+
+ # Calculate the batch_size
+ nbatch = nenvs * nsteps
+ nbatch_train = nbatch // nminibatches
+ is_mpi_root = (MPI is None or MPI.COMM_WORLD.Get_rank() == 0)
+
+ # Instantiate the model object (that creates act_model and train_model)
+ if model_fn is None:
+ from baselines.ppo2.model import Model
+ model_fn = Model
+
+ model = model_fn(ac_space=ac_space, policy_network=network, ent_coef=ent_coef, vf_coef=vf_coef,
+ max_grad_norm=max_grad_norm)
+
+ if load_path is not None:
+ load_path = osp.expanduser(load_path)
+ ckpt = tf.train.Checkpoint(model=model)
+ manager = tf.train.CheckpointManager(ckpt, load_path, max_to_keep=None)
+ ckpt.restore(manager.latest_checkpoint)
+
+ # Instantiate the runner object
+ runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
+ if eval_env is not None:
+ eval_runner = Runner(env = eval_env, model = model, nsteps = nsteps, gamma = gamma, lam= lam)
+
+ epinfobuf = deque(maxlen=100)
+ if eval_env is not None:
+ eval_epinfobuf = deque(maxlen=100)
+
+ # Start total timer
+ tfirststart = time.perf_counter()
+
+ nupdates = total_timesteps//nbatch
+ for update in range(1, nupdates+1):
+ assert nbatch % nminibatches == 0
+ # Start timer
+ tstart = time.perf_counter()
+ frac = 1.0 - (update - 1.0) / nupdates
+ # Calculate the learning rate
+ lrnow = lr(frac)
+ cliprangenow = cliprange(frac)
+
+ if update % log_interval == 0 and is_mpi_root: logger.info('Stepping environment...')
+
+ # Get minibatch
+ obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632
+ if eval_env is not None:
+ eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run() #pylint: disable=E0632
+
+ epinfobuf.extend(epinfos)
+ if eval_env is not None:
+ eval_epinfobuf.extend(eval_epinfos)
+
+ # Here what we're going to do is for each minibatch calculate the loss and append it.
+ mblossvals = []
+ if states is None: # nonrecurrent version
+ # Index of each element of batch_size
+ # Create the indices array
+ inds = np.arange(nbatch)
+ for _ in range(noptepochs):
+ # Randomize the indexes
+ np.random.shuffle(inds)
+ # 0 to batch_size with batch_train_size step
+ for start in range(0, nbatch, nbatch_train):
+ end = start + nbatch_train
+ mbinds = inds[start:end]
+ slices = (tf.constant(arr[mbinds]) for arr in (obs, returns, masks, actions, values, neglogpacs))
+ mblossvals.append(model.train(lrnow, cliprangenow, *slices))
+ else: # recurrent version
+ raise ValueError('Not Support Yet')
+
+ # Feedforward --> get losses --> update
+ lossvals = np.mean(mblossvals, axis=0)
+ # End timer
+ tnow = time.perf_counter()
+ # Calculate the fps (frame per second)
+ fps = int(nbatch / (tnow - tstart))
+ if update % log_interval == 0 or update == 1:
+ # Calculates if value function is a good predicator of the returns (ev > 1)
+ # or if it's just worse than predicting nothing (ev =< 0)
+ ev = explained_variance(values, returns)
+ logger.logkv("misc/serial_timesteps", update*nsteps)
+ logger.logkv("misc/nupdates", update)
+ logger.logkv("misc/total_timesteps", update*nbatch)
+ logger.logkv("fps", fps)
+ logger.logkv("misc/explained_variance", float(ev))
+ logger.logkv('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf]))
+ logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
+ if eval_env is not None:
+ logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf]) )
+ logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf]) )
+ logger.logkv('misc/time_elapsed', tnow - tfirststart)
+ for (lossval, lossname) in zip(lossvals, model.loss_names):
+ logger.logkv('loss/' + lossname, lossval)
+
+ logger.dumpkvs()
+
+ return model
+# Avoid division error when calculate the mean (in our case if epinfo is empty returns np.nan, not return an error)
+def safemean(xs):
+ return np.nan if len(xs) == 0 else np.mean(xs)
+
+
diff --git a/baselines/baselines/ppo2/runner.py b/baselines/baselines/ppo2/runner.py
new file mode 100644
index 0000000000000000000000000000000000000000..af5470b1555c0be7011f624d26e7f75515dd5182
--- /dev/null
+++ b/baselines/baselines/ppo2/runner.py
@@ -0,0 +1,79 @@
+import numpy as np
+import tensorflow as tf
+from baselines.common.runners import AbstractEnvRunner
+
+class Runner(AbstractEnvRunner):
+ """
+ We use this object to make a mini batch of experiences
+ __init__:
+ - Initialize the runner
+
+ run():
+ - Make a mini batch
+ """
+ def __init__(self, *, env, model, nsteps, gamma, lam):
+ super().__init__(env=env, model=model, nsteps=nsteps)
+ # Lambda used in GAE (General Advantage Estimation)
+ self.lam = lam
+ # Discount rate
+ self.gamma = gamma
+
+ def run(self):
+ # Here, we init the lists that will contain the mb of experiences
+ mb_obs, mb_rewards, mb_actions, mb_values, mb_dones, mb_neglogpacs = [],[],[],[],[],[]
+ mb_states = self.states
+ epinfos = []
+ # For n in range number of steps
+ for _ in range(self.nsteps):
+ # Given observations, get action value and neglopacs
+ # We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init
+ obs = tf.constant(self.obs)
+ actions, values, self.states, neglogpacs = self.model.step(obs)
+ actions = actions._numpy()
+ mb_obs.append(self.obs.copy())
+ mb_actions.append(actions)
+ mb_values.append(values._numpy())
+ mb_neglogpacs.append(neglogpacs._numpy())
+ mb_dones.append(self.dones)
+
+ # Take actions in env and look the results
+ # Infos contains a ton of useful informations
+ self.obs[:], rewards, self.dones, infos = self.env.step(actions)
+ for info in infos:
+ maybeepinfo = info.get('episode')
+ if maybeepinfo: epinfos.append(maybeepinfo)
+ mb_rewards.append(rewards)
+
+ #batch of steps to batch of rollouts
+ mb_obs = np.asarray(mb_obs, dtype=self.obs.dtype)
+ mb_rewards = np.asarray(mb_rewards, dtype=np.float32)
+ mb_actions = np.asarray(mb_actions)
+ mb_values = np.asarray(mb_values, dtype=np.float32)
+ mb_neglogpacs = np.asarray(mb_neglogpacs, dtype=np.float32)
+ mb_dones = np.asarray(mb_dones, dtype=np.bool)
+ last_values = self.model.value(tf.constant(self.obs))._numpy()
+
+ # discount/bootstrap off value fn
+ mb_returns = np.zeros_like(mb_rewards)
+ mb_advs = np.zeros_like(mb_rewards)
+ lastgaelam = 0
+ for t in reversed(range(self.nsteps)):
+ if t == self.nsteps - 1:
+ nextnonterminal = 1.0 - self.dones
+ nextvalues = last_values
+ else:
+ nextnonterminal = 1.0 - mb_dones[t+1]
+ nextvalues = mb_values[t+1]
+ delta = mb_rewards[t] + self.gamma * nextvalues * nextnonterminal - mb_values[t]
+ mb_advs[t] = lastgaelam = delta + self.gamma * self.lam * nextnonterminal * lastgaelam
+ mb_returns = mb_advs + mb_values
+ return (*map(sf01, (mb_obs, mb_returns, mb_dones, mb_actions, mb_values, mb_neglogpacs)),
+ mb_states, epinfos)
+
+
+def sf01(arr):
+ """
+ swap and then flatten axes 0 and 1
+ """
+ s = arr.shape
+ return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])
diff --git a/baselines/baselines/results_plotter.py b/baselines/baselines/results_plotter.py
new file mode 100644
index 0000000000000000000000000000000000000000..66f09bd12d92accb8f989579bb347a19ab0acdd0
--- /dev/null
+++ b/baselines/baselines/results_plotter.py
@@ -0,0 +1,94 @@
+import numpy as np
+import matplotlib
+matplotlib.use('TkAgg') # Can change to 'Agg' for non-interactive mode
+
+import matplotlib.pyplot as plt
+plt.rcParams['svg.fonttype'] = 'none'
+
+from baselines.common import plot_util
+
+X_TIMESTEPS = 'timesteps'
+X_EPISODES = 'episodes'
+X_WALLTIME = 'walltime_hrs'
+Y_REWARD = 'reward'
+Y_TIMESTEPS = 'timesteps'
+POSSIBLE_X_AXES = [X_TIMESTEPS, X_EPISODES, X_WALLTIME]
+EPISODES_WINDOW = 100
+COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'purple', 'pink',
+ 'brown', 'orange', 'teal', 'coral', 'lightblue', 'lime', 'lavender', 'turquoise',
+ 'darkgreen', 'tan', 'salmon', 'gold', 'darkred', 'darkblue']
+
+def rolling_window(a, window):
+ shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
+ strides = a.strides + (a.strides[-1],)
+ return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
+
+def window_func(x, y, window, func):
+ yw = rolling_window(y, window)
+ yw_func = func(yw, axis=-1)
+ return x[window-1:], yw_func
+
+def ts2xy(ts, xaxis, yaxis):
+ if xaxis == X_TIMESTEPS:
+ x = np.cumsum(ts.l.values)
+ elif xaxis == X_EPISODES:
+ x = np.arange(len(ts))
+ elif xaxis == X_WALLTIME:
+ x = ts.t.values / 3600.
+ else:
+ raise NotImplementedError
+ if yaxis == Y_REWARD:
+ y = ts.r.values
+ elif yaxis == Y_TIMESTEPS:
+ y = ts.l.values
+ else:
+ raise NotImplementedError
+ return x, y
+
+def plot_curves(xy_list, xaxis, yaxis, title):
+ fig = plt.figure(figsize=(8,2))
+ maxx = max(xy[0][-1] for xy in xy_list)
+ minx = 0
+ for (i, (x, y)) in enumerate(xy_list):
+ color = COLORS[i % len(COLORS)]
+ plt.scatter(x, y, s=2)
+ x, y_mean = window_func(x, y, EPISODES_WINDOW, np.mean) #So returns average of last EPISODE_WINDOW episodes
+ plt.plot(x, y_mean, color=color)
+ plt.xlim(minx, maxx)
+ plt.title(title)
+ plt.xlabel(xaxis)
+ plt.ylabel(yaxis)
+ plt.tight_layout()
+ fig.canvas.mpl_connect('resize_event', lambda event: plt.tight_layout())
+ plt.grid(True)
+
+
+def split_by_task(taskpath):
+ return taskpath['dirname'].split('/')[-1].split('-')[0]
+
+def plot_results(dirs, num_timesteps=10e6, xaxis=X_TIMESTEPS, yaxis=Y_REWARD, title='', split_fn=split_by_task):
+ results = plot_util.load_results(dirs)
+ plot_util.plot_results(results, xy_fn=lambda r: ts2xy(r['monitor'], xaxis, yaxis), split_fn=split_fn, average_group=True, resample=int(1e6))
+
+# Example usage in jupyter-notebook
+# from baselines.results_plotter import plot_results
+# %matplotlib inline
+# plot_results("./log")
+# Here ./log is a directory containing the monitor.csv files
+
+def main():
+ import argparse
+ import os
+ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ parser.add_argument('--dirs', help='List of log directories', nargs = '*', default=['./log'])
+ parser.add_argument('--num_timesteps', type=int, default=int(10e6))
+ parser.add_argument('--xaxis', help = 'Varible on X-axis', default = X_TIMESTEPS)
+ parser.add_argument('--yaxis', help = 'Varible on Y-axis', default = Y_REWARD)
+ parser.add_argument('--task_name', help = 'Title of plot', default = 'Breakout')
+ args = parser.parse_args()
+ args.dirs = [os.path.abspath(dir) for dir in args.dirs]
+ plot_results(args.dirs, args.num_timesteps, args.xaxis, args.yaxis, args.task_name)
+ plt.show()
+
+if __name__ == '__main__':
+ main()
diff --git a/baselines/baselines/run.py b/baselines/baselines/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbf24535b7dc38e155dbce607e0f1f9f04652d80
--- /dev/null
+++ b/baselines/baselines/run.py
@@ -0,0 +1,248 @@
+import sys
+import re
+import multiprocessing
+import os.path as osp
+import gym
+from collections import defaultdict
+import tensorflow as tf
+import numpy as np
+
+from baselines.common.vec_env import VecFrameStack, VecNormalize, VecEnv
+from baselines.common.vec_env.vec_video_recorder import VecVideoRecorder
+from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_vec_env, make_env
+from baselines import logger
+from importlib import import_module
+
+try:
+ from mpi4py import MPI
+except ImportError:
+ MPI = None
+
+try:
+ import pybullet_envs
+except ImportError:
+ pybullet_envs = None
+
+try:
+ import roboschool
+except ImportError:
+ roboschool = None
+
+_game_envs = defaultdict(set)
+for env in gym.envs.registry.all():
+ # TODO: solve this with regexes
+ env_type = env.entry_point.split(':')[0].split('.')[-1]
+ _game_envs[env_type].add(env.id)
+
+# reading benchmark names directly from retro requires
+# importing retro here, and for some reason that crashes tensorflow
+# in ubuntu
+_game_envs['retro'] = {
+ 'BubbleBobble-Nes',
+ 'SuperMarioBros-Nes',
+ 'TwinBee3PokoPokoDaimaou-Nes',
+ 'SpaceHarrier-Nes',
+ 'SonicTheHedgehog-Genesis',
+ 'Vectorman-Genesis',
+ 'FinalFight-Snes',
+ 'SpaceInvaders-Snes',
+}
+
+
+def train(args, extra_args):
+ env_type, env_id = get_env_type(args)
+ print('env_type: {}'.format(env_type))
+
+ total_timesteps = int(args.num_timesteps)
+ seed = args.seed
+
+ learn = get_learn_function(args.alg)
+ alg_kwargs = get_learn_function_defaults(args.alg, env_type)
+ alg_kwargs.update(extra_args)
+
+ env = build_env(args)
+ if args.save_video_interval != 0:
+ env = VecVideoRecorder(env, osp.join(logger.get_dir(), "videos"), record_video_trigger=lambda x: x % args.save_video_interval == 0, video_length=args.save_video_length)
+
+ if args.network:
+ alg_kwargs['network'] = args.network
+ else:
+ if alg_kwargs.get('network') is None:
+ alg_kwargs['network'] = get_default_network(env_type)
+
+ print('Training {} on {}:{} with arguments \n{}'.format(args.alg, env_type, env_id, alg_kwargs))
+
+ model = learn(
+ env=env,
+ seed=seed,
+ total_timesteps=total_timesteps,
+ **alg_kwargs
+ )
+
+ return model, env
+
+
+def build_env(args):
+ ncpu = multiprocessing.cpu_count()
+ if sys.platform == 'darwin': ncpu //= 2
+ nenv = args.num_env or ncpu
+ alg = args.alg
+ seed = args.seed
+
+ env_type, env_id = get_env_type(args)
+
+ if env_type in {'atari', 'retro'}:
+ if alg == 'deepq':
+ env = make_env(env_id, env_type, seed=seed, wrapper_kwargs={'frame_stack': True})
+ elif alg == 'trpo_mpi':
+ env = make_env(env_id, env_type, seed=seed)
+ else:
+ frame_stack_size = 4
+ env = make_vec_env(env_id, env_type, nenv, seed, gamestate=args.gamestate, reward_scale=args.reward_scale)
+ env = VecFrameStack(env, frame_stack_size)
+
+ else:
+ flatten_dict_observations = alg not in {'her'}
+ env = make_vec_env(env_id, env_type, args.num_env or 1, seed, reward_scale=args.reward_scale, flatten_dict_observations=flatten_dict_observations)
+
+ if env_type == 'mujoco':
+ env = VecNormalize(env)
+
+ return env
+
+
+def get_env_type(args):
+ env_id = args.env
+
+ if args.env_type is not None:
+ return args.env_type, env_id
+
+ # Re-parse the gym registry, since we could have new envs since last time.
+ for env in gym.envs.registry.all():
+ env_type = env.entry_point.split(':')[0].split('.')[-1]
+ _game_envs[env_type].add(env.id) # This is a set so add is idempotent
+
+ if env_id in _game_envs.keys():
+ env_type = env_id
+ env_id = [g for g in _game_envs[env_type]][0]
+ else:
+ env_type = None
+ for g, e in _game_envs.items():
+ if env_id in e:
+ env_type = g
+ break
+ if ':' in env_id:
+ env_type = re.sub(r':.*', '', env_id)
+ assert env_type is not None, 'env_id {} is not recognized in env types'.format(env_id, _game_envs.keys())
+
+ return env_type, env_id
+
+
+def get_default_network(env_type):
+ if env_type in {'atari', 'retro'}:
+ return 'cnn'
+ else:
+ return 'mlp'
+
+def get_alg_module(alg, submodule=None):
+ submodule = submodule or alg
+ try:
+ # first try to import the alg module from baselines
+ alg_module = import_module('.'.join(['baselines', alg, submodule]))
+ except ImportError:
+ # then from rl_algs
+ alg_module = import_module('.'.join(['rl_' + 'algs', alg, submodule]))
+
+ return alg_module
+
+
+def get_learn_function(alg):
+ return get_alg_module(alg).learn
+
+
+def get_learn_function_defaults(alg, env_type):
+ try:
+ alg_defaults = get_alg_module(alg, 'defaults')
+ kwargs = getattr(alg_defaults, env_type)()
+ except (ImportError, AttributeError):
+ kwargs = {}
+ return kwargs
+
+
+
+def parse_cmdline_kwargs(args):
+ '''
+ convert a list of '='-spaced command-line arguments to a dictionary, evaluating python objects when possible
+ '''
+ def parse(v):
+
+ assert isinstance(v, str)
+ try:
+ return eval(v)
+ except (NameError, SyntaxError):
+ return v
+
+ return {k: parse(v) for k,v in parse_unknown_args(args).items()}
+
+
+def configure_logger(log_path, **kwargs):
+ if log_path is not None:
+ logger.configure(log_path)
+ else:
+ logger.configure(**kwargs)
+
+
+def main(args):
+ # configure logger, disable logging in child MPI processes (with rank > 0)
+
+ arg_parser = common_arg_parser()
+ args, unknown_args = arg_parser.parse_known_args(args)
+ extra_args = parse_cmdline_kwargs(unknown_args)
+
+ if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
+ rank = 0
+ configure_logger(args.log_path)
+ else:
+ rank = MPI.COMM_WORLD.Get_rank()
+ configure_logger(args.log_path, format_strs=[])
+
+ model, env = train(args, extra_args)
+
+ if args.save_path is not None and rank == 0:
+ save_path = osp.expanduser(args.save_path)
+ ckpt = tf.train.Checkpoint(model=model)
+ manager = tf.train.CheckpointManager(ckpt, save_path, max_to_keep=None)
+ manager.save()
+
+ if args.play:
+ logger.log("Running trained model")
+ obs = env.reset()
+ if not isinstance(env, VecEnv):
+ obs = np.expand_dims(np.array(obs), axis=0)
+
+ state = model.initial_state if hasattr(model, 'initial_state') else None
+
+ episode_rew = np.zeros(env.num_envs) if isinstance(env, VecEnv) else np.zeros(1)
+ while True:
+ if state is not None:
+ actions, _, state, _ = model.step(obs)
+ else:
+ actions, _, _, _ = model.step(obs)
+
+ obs, rew, done, _ = env.step(actions.numpy())
+ if not isinstance(env, VecEnv):
+ obs = np.expand_dims(np.array(obs), axis=0)
+ episode_rew += rew
+ env.render()
+ done_any = done.any() if isinstance(done, np.ndarray) else done
+ if done_any:
+ for i in np.nonzero(done)[0]:
+ print('episode_rew={}'.format(episode_rew[i]))
+ episode_rew[i] = 0
+
+ env.close()
+
+ return model
+
+if __name__ == '__main__':
+ main(sys.argv)
diff --git a/baselines/baselines/trpo_mpi/README.md b/baselines/baselines/trpo_mpi/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4cdbb5aee2d09cb4d38e0d237fbdac6576e529bf
--- /dev/null
+++ b/baselines/baselines/trpo_mpi/README.md
@@ -0,0 +1,7 @@
+# trpo_mpi
+
+- Original paper: https://arxiv.org/abs/1502.05477
+- Baselines blog post https://blog.openai.com/openai-baselines-ppo/
+- `mpirun -np 16 python -m baselines.run --alg=trpo_mpi --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
+- `python -m baselines.run --alg=trpo_mpi --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M timesteps on a Mujoco Ant environment.
+- also refer to the repo-wide [README.md](../../README.md#training-models)
diff --git a/baselines/baselines/trpo_mpi/__init__.py b/baselines/baselines/trpo_mpi/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/baselines/baselines/trpo_mpi/defaults.py b/baselines/baselines/trpo_mpi/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ac3f7af35eb4c39eb4a3ca64438189781bd63eb
--- /dev/null
+++ b/baselines/baselines/trpo_mpi/defaults.py
@@ -0,0 +1,30 @@
+from baselines.common.models import mlp, cnn_small
+
+
+def atari():
+ return dict(
+ network = cnn_small(),
+ timesteps_per_batch=512,
+ max_kl=0.001,
+ cg_iters=10,
+ cg_damping=1e-3,
+ gamma=0.98,
+ lam=1.0,
+ vf_iters=3,
+ vf_stepsize=1e-4,
+ entcoeff=0.00,
+ )
+
+def mujoco():
+ return dict(
+ network = mlp(num_hidden=32, num_layers=2),
+ timesteps_per_batch=1024,
+ max_kl=0.01,
+ cg_iters=10,
+ cg_damping=0.1,
+ gamma=0.99,
+ lam=0.98,
+ vf_iters=5,
+ vf_stepsize=1e-3,
+ normalize_observations=True,
+ )
diff --git a/baselines/baselines/trpo_mpi/trpo_mpi.py b/baselines/baselines/trpo_mpi/trpo_mpi.py
new file mode 100644
index 0000000000000000000000000000000000000000..d131aaa38c3196cfa46f55d4aafd892e5467a48b
--- /dev/null
+++ b/baselines/baselines/trpo_mpi/trpo_mpi.py
@@ -0,0 +1,448 @@
+from baselines.common import explained_variance, zipsame, dataset
+from baselines import logger
+import baselines.common.tf_util as U
+import tensorflow as tf, numpy as np
+import time
+import os.path as osp
+from baselines.common import colorize
+from collections import deque
+from baselines.common import set_global_seeds
+from baselines.common.models import get_network_builder
+from baselines.common.mpi_adam import MpiAdam
+from baselines.common.cg import cg
+from baselines.common.policies import PolicyWithValue
+from baselines.common.vec_env.vec_env import VecEnv
+from contextlib import contextmanager
+
+try:
+ from mpi4py import MPI
+except ImportError:
+ MPI = None
+
+def traj_segment_generator(pi, env, horizon):
+ # Initialize state variables
+ t = 0
+ ac = env.action_space.sample()
+ new = True
+ rew = 0.0
+ ob = env.reset()
+ if not isinstance(env, VecEnv):
+ ob = np.expand_dims(ob, axis=0)
+
+ cur_ep_ret = 0
+ cur_ep_len = 0
+ ep_rets = []
+ ep_lens = []
+
+ # Initialize history arrays
+ obs = np.array([ob for _ in range(horizon)])
+ rews = np.zeros(horizon, 'float32')
+ vpreds = np.zeros(horizon, 'float32')
+ news = np.zeros(horizon, 'int32')
+ acs = np.array([ac for _ in range(horizon)])
+ prevacs = acs.copy()
+
+ while True:
+ prevac = ac
+ ob = tf.constant(ob)
+ ac, vpred, _, _ = pi.step(ob)
+ ac = ac.numpy()
+ # Slight weirdness here because we need value function at time T
+ # before returning segment [0, T-1] so we get the correct
+ # terminal value
+ if t > 0 and t % horizon == 0:
+ yield {"ob" : obs, "rew" : rews, "vpred" : vpreds, "new" : news,
+ "ac" : acs, "prevac" : prevacs, "nextvpred": vpred * (1 - new),
+ "ep_rets" : ep_rets, "ep_lens" : ep_lens}
+ _, vpred, _, _ = pi.step(ob)
+ # Be careful!!! if you change the downstream algorithm to aggregate
+ # several of these batches, then be sure to do a deepcopy
+ ep_rets = []
+ ep_lens = []
+ i = t % horizon
+ obs[i] = ob
+ vpreds[i] = vpred.numpy()
+ news[i] = new
+ acs[i] = ac
+ prevacs[i] = prevac
+
+ ob, rew, new, _ = env.step(ac)
+ if not isinstance(env, VecEnv):
+ ob = np.expand_dims(ob, axis=0)
+ rews[i] = rew
+
+ cur_ep_ret += rew
+ cur_ep_len += 1
+ if new:
+ ep_rets.append(cur_ep_ret)
+ ep_lens.append(cur_ep_len)
+ cur_ep_ret = 0
+ cur_ep_len = 0
+ ob = env.reset()
+ if not isinstance(env, VecEnv):
+ ob = np.expand_dims(ob, axis=0)
+ t += 1
+
+def add_vtarg_and_adv(seg, gamma, lam):
+ new = np.append(seg["new"], 0) # last element is only used for last vtarg, but we already zeroed it if last new = 1
+ vpred = np.append(seg["vpred"], seg["nextvpred"])
+ T = len(seg["rew"])
+ seg["adv"] = gaelam = np.empty(T, 'float32')
+ rew = seg["rew"]
+ lastgaelam = 0
+ for t in reversed(range(T)):
+ nonterminal = 1-new[t+1]
+ delta = rew[t] + gamma * vpred[t+1] * nonterminal - vpred[t]
+ gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam
+ seg["tdlamret"] = seg["adv"] + seg["vpred"]
+
+def learn(*,
+ network,
+ env,
+ total_timesteps,
+ timesteps_per_batch=1024, # what to train on
+ max_kl=0.001,
+ cg_iters=10,
+ gamma=0.99,
+ lam=1.0, # advantage estimation
+ seed=None,
+ ent_coef=0.0,
+ cg_damping=1e-2,
+ vf_stepsize=3e-4,
+ vf_iters =3,
+ max_episodes=0, max_iters=0, # time constraint
+ callback=None,
+ load_path=None,
+ **network_kwargs
+ ):
+ '''
+ learn a policy function with TRPO algorithm
+
+ Parameters:
+ ----------
+
+ network neural network to learn. Can be either string ('mlp', 'cnn', 'lstm', 'lnlstm' for basic types)
+ or function that takes input placeholder and returns tuple (output, None) for feedforward nets
+ or (output, (state_placeholder, state_output, mask_placeholder)) for recurrent nets
+
+ env environment (one of the gym environments or wrapped via baselines.common.vec_env.VecEnv-type class
+
+ timesteps_per_batch timesteps per gradient estimation batch
+
+ max_kl max KL divergence between old policy and new policy ( KL(pi_old || pi) )
+
+ ent_coef coefficient of policy entropy term in the optimization objective
+
+ cg_iters number of iterations of conjugate gradient algorithm
+
+ cg_damping conjugate gradient damping
+
+ vf_stepsize learning rate for adam optimizer used to optimie value function loss
+
+ vf_iters number of iterations of value function optimization iterations per each policy optimization step
+
+ total_timesteps max number of timesteps
+
+ max_episodes max number of episodes
+
+ max_iters maximum number of policy optimization iterations
+
+ callback function to be called with (locals(), globals()) each policy optimization step
+
+ load_path str, path to load the model from (default: None, i.e. no model is loaded)
+
+ **network_kwargs keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
+
+ Returns:
+ -------
+
+ learnt model
+
+ '''
+
+ if MPI is not None:
+ nworkers = MPI.COMM_WORLD.Get_size()
+ rank = MPI.COMM_WORLD.Get_rank()
+ else:
+ nworkers = 1
+ rank = 0
+
+ set_global_seeds(seed)
+
+ np.set_printoptions(precision=3)
+ # Setup losses and stuff
+ # ----------------------------------------
+ ob_space = env.observation_space
+ ac_space = env.action_space
+
+ if isinstance(network, str):
+ network = get_network_builder(network)(**network_kwargs)
+
+ with tf.name_scope("pi"):
+ pi_policy_network = network(ob_space.shape)
+ pi_value_network = network(ob_space.shape)
+ pi = PolicyWithValue(ac_space, pi_policy_network, pi_value_network)
+ with tf.name_scope("oldpi"):
+ old_pi_policy_network = network(ob_space.shape)
+ old_pi_value_network = network(ob_space.shape)
+ oldpi = PolicyWithValue(ac_space, old_pi_policy_network, old_pi_value_network)
+
+ pi_var_list = pi_policy_network.trainable_variables + list(pi.pdtype.trainable_variables)
+ old_pi_var_list = old_pi_policy_network.trainable_variables + list(oldpi.pdtype.trainable_variables)
+ vf_var_list = pi_value_network.trainable_variables + pi.value_fc.trainable_variables
+ old_vf_var_list = old_pi_value_network.trainable_variables + oldpi.value_fc.trainable_variables
+
+ if load_path is not None:
+ load_path = osp.expanduser(load_path)
+ ckpt = tf.train.Checkpoint(model=pi)
+ manager = tf.train.CheckpointManager(ckpt, load_path, max_to_keep=None)
+ ckpt.restore(manager.latest_checkpoint)
+
+ vfadam = MpiAdam(vf_var_list)
+
+ get_flat = U.GetFlat(pi_var_list)
+ set_from_flat = U.SetFromFlat(pi_var_list)
+ loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
+ shapes = [var.get_shape().as_list() for var in pi_var_list]
+
+
+ def assign_old_eq_new():
+ for pi_var, old_pi_var in zip(pi_var_list, old_pi_var_list):
+ old_pi_var.assign(pi_var)
+ for vf_var, old_vf_var in zip(vf_var_list, old_vf_var_list):
+ old_vf_var.assign(vf_var)
+
+ @tf.function
+ def compute_lossandgrad(ob, ac, atarg):
+ with tf.GradientTape() as tape:
+ old_policy_latent = oldpi.policy_network(ob)
+ old_pd, _ = oldpi.pdtype.pdfromlatent(old_policy_latent)
+ policy_latent = pi.policy_network(ob)
+ pd, _ = pi.pdtype.pdfromlatent(policy_latent)
+ kloldnew = old_pd.kl(pd)
+ ent = pd.entropy()
+ meankl = tf.reduce_mean(kloldnew)
+ meanent = tf.reduce_mean(ent)
+ entbonus = ent_coef * meanent
+ ratio = tf.exp(pd.logp(ac) - old_pd.logp(ac))
+ surrgain = tf.reduce_mean(ratio * atarg)
+ optimgain = surrgain + entbonus
+ losses = [optimgain, meankl, entbonus, surrgain, meanent]
+ gradients = tape.gradient(optimgain, pi_var_list)
+ return losses + [U.flatgrad(gradients, pi_var_list)]
+
+ @tf.function
+ def compute_losses(ob, ac, atarg):
+ old_policy_latent = oldpi.policy_network(ob)
+ old_pd, _ = oldpi.pdtype.pdfromlatent(old_policy_latent)
+ policy_latent = pi.policy_network(ob)
+ pd, _ = pi.pdtype.pdfromlatent(policy_latent)
+ kloldnew = old_pd.kl(pd)
+ ent = pd.entropy()
+ meankl = tf.reduce_mean(kloldnew)
+ meanent = tf.reduce_mean(ent)
+ entbonus = ent_coef * meanent
+ ratio = tf.exp(pd.logp(ac) - old_pd.logp(ac))
+ surrgain = tf.reduce_mean(ratio * atarg)
+ optimgain = surrgain + entbonus
+ losses = [optimgain, meankl, entbonus, surrgain, meanent]
+ return losses
+
+ #ob shape should be [batch_size, ob_dim], merged nenv
+ #ret shape should be [batch_size]
+ @tf.function
+ def compute_vflossandgrad(ob, ret):
+ with tf.GradientTape() as tape:
+ pi_vf = pi.value(ob)
+ vferr = tf.reduce_mean(tf.square(pi_vf - ret))
+ return U.flatgrad(tape.gradient(vferr, vf_var_list), vf_var_list)
+
+ @tf.function
+ def compute_fvp(flat_tangent, ob, ac, atarg):
+ with tf.GradientTape() as outter_tape:
+ with tf.GradientTape() as inner_tape:
+ old_policy_latent = oldpi.policy_network(ob)
+ old_pd, _ = oldpi.pdtype.pdfromlatent(old_policy_latent)
+ policy_latent = pi.policy_network(ob)
+ pd, _ = pi.pdtype.pdfromlatent(policy_latent)
+ kloldnew = old_pd.kl(pd)
+ meankl = tf.reduce_mean(kloldnew)
+ klgrads = inner_tape.gradient(meankl, pi_var_list)
+ start = 0
+ tangents = []
+ for shape in shapes:
+ sz = U.intprod(shape)
+ tangents.append(tf.reshape(flat_tangent[start:start+sz], shape))
+ start += sz
+ gvp = tf.add_n([tf.reduce_sum(g*tangent) for (g, tangent) in zipsame(klgrads, tangents)])
+ hessians_products = outter_tape.gradient(gvp, pi_var_list)
+ fvp = U.flatgrad(hessians_products, pi_var_list)
+ return fvp
+
+ @contextmanager
+ def timed(msg):
+ if rank == 0:
+ print(colorize(msg, color='magenta'))
+ tstart = time.time()
+ yield
+ print(colorize("done in %.3f seconds"%(time.time() - tstart), color='magenta'))
+ else:
+ yield
+
+ def allmean(x):
+ assert isinstance(x, np.ndarray)
+ if MPI is not None:
+ out = np.empty_like(x)
+ MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
+ out /= nworkers
+ else:
+ out = np.copy(x)
+
+ return out
+
+ th_init = get_flat()
+ if MPI is not None:
+ MPI.COMM_WORLD.Bcast(th_init, root=0)
+
+ set_from_flat(th_init)
+ vfadam.sync()
+ print("Init param sum", th_init.sum(), flush=True)
+
+ # Prepare for rollouts
+ # ----------------------------------------
+ seg_gen = traj_segment_generator(pi, env, timesteps_per_batch)
+
+ episodes_so_far = 0
+ timesteps_so_far = 0
+ iters_so_far = 0
+ tstart = time.time()
+ lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
+ rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
+
+ if sum([max_iters>0, total_timesteps>0, max_episodes>0])==0:
+ # noththing to be done
+ return pi
+
+ assert sum([max_iters>0, total_timesteps>0, max_episodes>0]) < 2, \
+ 'out of max_iters, total_timesteps, and max_episodes only one should be specified'
+
+ while True:
+ if callback: callback(locals(), globals())
+ if total_timesteps and timesteps_so_far >= total_timesteps:
+ break
+ elif max_episodes and episodes_so_far >= max_episodes:
+ break
+ elif max_iters and iters_so_far >= max_iters:
+ break
+ logger.log("********** Iteration %i ************"%iters_so_far)
+
+ with timed("sampling"):
+ seg = seg_gen.__next__()
+ add_vtarg_and_adv(seg, gamma, lam)
+
+ # ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
+ ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"]
+ ob = sf01(ob)
+ vpredbefore = seg["vpred"] # predicted value function before udpate
+ atarg = (atarg - atarg.mean()) / atarg.std() # standardized advantage function estimate
+
+ if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret)
+ if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy
+
+ args = ob, ac, atarg
+ fvpargs = [arr[::5] for arr in args]
+ def fisher_vector_product(p):
+ return allmean(compute_fvp(p, *fvpargs).numpy()) + cg_damping * p
+
+ assign_old_eq_new() # set old parameter values to new parameter values
+ with timed("computegrad"):
+ *lossbefore, g = compute_lossandgrad(*args)
+ lossbefore = allmean(np.array(lossbefore))
+ g = g.numpy()
+ g = allmean(g)
+ if np.allclose(g, 0):
+ logger.log("Got zero gradient. not updating")
+ else:
+ with timed("cg"):
+ stepdir = cg(fisher_vector_product, g, cg_iters=cg_iters, verbose=rank==0)
+ assert np.isfinite(stepdir).all()
+ shs = .5*stepdir.dot(fisher_vector_product(stepdir))
+ lm = np.sqrt(shs / max_kl)
+ # logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
+ fullstep = stepdir / lm
+ expectedimprove = g.dot(fullstep)
+ surrbefore = lossbefore[0]
+ stepsize = 1.0
+ thbefore = get_flat()
+ for _ in range(10):
+ thnew = thbefore + fullstep * stepsize
+ set_from_flat(thnew)
+ meanlosses = surr, kl, *_ = allmean(np.array(compute_losses(*args)))
+ improve = surr - surrbefore
+ logger.log("Expected: %.3f Actual: %.3f"%(expectedimprove, improve))
+ if not np.isfinite(meanlosses).all():
+ logger.log("Got non-finite value of losses -- bad!")
+ elif kl > max_kl * 1.5:
+ logger.log("violated KL constraint. shrinking step.")
+ elif improve < 0:
+ logger.log("surrogate didn't improve. shrinking step.")
+ else:
+ logger.log("Stepsize OK!")
+ break
+ stepsize *= .5
+ else:
+ logger.log("couldn't compute a good step")
+ set_from_flat(thbefore)
+ if nworkers > 1 and iters_so_far % 20 == 0:
+ paramsums = MPI.COMM_WORLD.allgather((thnew.sum(), vfadam.getflat().sum())) # list of tuples
+ assert all(np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
+
+ for (lossname, lossval) in zip(loss_names, meanlosses):
+ logger.record_tabular(lossname, lossval)
+
+ with timed("vf"):
+
+ for _ in range(vf_iters):
+ for (mbob, mbret) in dataset.iterbatches((seg["ob"], seg["tdlamret"]),
+ include_final_partial_batch=False, batch_size=64):
+ mbob = sf01(mbob)
+ g = allmean(compute_vflossandgrad(mbob, mbret).numpy())
+ vfadam.update(g, vf_stepsize)
+
+ logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret))
+
+ lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
+ if MPI is not None:
+ listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
+ else:
+ listoflrpairs = [lrlocal]
+
+ lens, rews = map(flatten_lists, zip(*listoflrpairs))
+ lenbuffer.extend(lens)
+ rewbuffer.extend(rews)
+
+ logger.record_tabular("EpLenMean", np.mean(lenbuffer))
+ logger.record_tabular("EpRewMean", np.mean(rewbuffer))
+ logger.record_tabular("EpThisIter", len(lens))
+ episodes_so_far += len(lens)
+ timesteps_so_far += sum(lens)
+ iters_so_far += 1
+
+ logger.record_tabular("EpisodesSoFar", episodes_so_far)
+ logger.record_tabular("TimestepsSoFar", timesteps_so_far)
+ logger.record_tabular("TimeElapsed", time.time() - tstart)
+
+ if rank==0:
+ logger.dump_tabular()
+
+ return pi
+
+def flatten_lists(listoflists):
+ return [el for list_ in listoflists for el in list_]
+
+def sf01(arr):
+ """
+ swap and then flatten axes 0 and 1
+ """
+ s = arr.shape
+ return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])
diff --git a/baselines/benchmarks_atari10M.htm b/baselines/benchmarks_atari10M.htm
new file mode 100644
index 0000000000000000000000000000000000000000..626fc0b4896bda590dc7ed68cce1b5cea59545db
--- /dev/null
+++ b/baselines/benchmarks_atari10M.htm
@@ -0,0 +1,16789 @@
+<html lang="en">
+<head>
+ <meta charset="utf-8">
+ <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+ <meta name="description" content="">
+ <meta name="author" content="">
+ <link rel="icon" href="https://cloud.githubusercontent.com/assets/306655/25257645/38a30806-25ee-11e7-82d5-eae86a9dfad0.png">
+ <title>bench viewer</title>
+
+ <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.1.1/jquery.min.js"></script>
+ <script src="https://cdnjs.cloudflare.com/ajax/libs/tether/1.4.0/js/tether.min.js"
+ integrity="sha384-DztdAPBWPRXSA/3eYEEUWrWCy7G5KFbe8fFjk5JAIxUYHKkDx6Qin1DkWx51bBrb"
+ crossorigin="anonymous"></script>
+ <script scr="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.3/umd/popper.min.js"></script>
+ <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-alpha.6/js/bootstrap.min.js"
+ integrity="sha384-vBWWzlZJ8ea9aCX4pEW3rVHjgjt7zpkNpZk+02D9phzyeVkE+jo0ieGizqPLForn"
+ crossorigin="anonymous"></script>
+ <script src="https://cdnjs.cloudflare.com/ajax/libs/datatables/1.10.12/js/jquery.dataTables.min.js"></script>
+ <script src="https://cdnjs.cloudflare.com/ajax/libs/datatables/1.10.13/js/dataTables.bootstrap4.min.js"></script>
+
+
+ <link rel="stylesheet"
+ href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css"
+ integrity="sha384-rwoIResjU2yc3z8GV/NPeZWAv56rSmLldC3R/AZzGRnGxQQKnKkoFVhFQhNUwEyJ"
+ crossorigin="anonymous">
+ <link href="https://cdnjs.cloudflare.com/ajax/libs/datatables/1.10.12/css/dataTables.bootstrap4.min.css"
+ rel="stylesheet"/>
+ <style>
+ .navbar {
+ border-radius: 0;
+ }
+
+ .benchmark-title {
+ padding: 3rem 1.5rem;
+ text-align: center;
+ margin-top: 40px;
+ }
+ .ci-section {
+ margin-top: 20px;
+ text-align: center;
+ }
+ .bmrun-link {
+ font-family: Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;
+ color: #fff; !important;
+ text-decoration: none; !important;
+ padding: 0.5em .4em;
+ }
+ .bmrun-link:hover {
+ color: #fff; !important;
+ text-decoration: none; !important;
+ }
+ </style>
+</head>
+
+<body>
+
+<div class="container">
+
+ <div class="benchmark-title">
+ <h1>Atari10M Comparison</h1>
+ </div>
+ <div style="text-align: center">
+</div>
+
+ <table class="table sortable-data-table">
+ <thead>
+ <tr>
+ <th> algo </th>
+
+ <td> </td>
+
+ <td>user</td>
+
+ <td>mean</td>
+
+ <td>Enduro</td>
+
+ <td>SpaceInvaders</td>
+
+ <td>Qbert</td>
+
+ <td>Seaquest</td>
+
+ <td>Pong</td>
+
+ <td>BeamRider</td>
+
+ <td>Breakout</td>
+
+ <td>commit</td>
+
+ </tr>
+ </thead>
+
+ <tr>
+ <td style="display: flex">
+
+ <div style="display: flex">
+ ppo2
+ </div>
+ </td>
+
+ <td><div style="background-color:rgba(102, 194, 165, 1.0)"> </div></td>
+
+ <td>cron</td>
+
+ <td>1509.38</td>
+
+ <td>350.22</td>
+
+ <td>557.28</td>
+
+ <td>7012.06</td>
+
+ <td>1218.87</td>
+
+ <td>13.68</td>
+
+ <td>1299.25</td>
+
+ <td>114.26</td>
+
+ <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+
+ </tr>
+
+ <tr>
+ <td style="display: flex">
+
+ <div style="display: flex">
+ deepq
+ </div>
+ </td>
+
+ <td><div style="background-color:rgba(252, 141, 98, 1.0)"> </div></td>
+
+ <td>cron</td>
+
+ <td>1012.69</td>
+
+ <td>479.75</td>
+
+ <td>459.86</td>
+
+ <td>3254.83</td>
+
+ <td>1164.08</td>
+
+ <td>16.49</td>
+
+ <td>1582.34</td>
+
+ <td>131.46</td>
+
+ <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+
+ </tr>
+
+ <tr>
+ <td style="display: flex">
+
+ <div style="display: flex">
+ acktr
+ </div>
+ </td>
+
+ <td><div style="background-color:rgba(141, 160, 203, 1.0)"> </div></td>
+
+ <td>cron</td>
+
+ <td>1211.71</td>
+
+ <td>0.0</td>
+
+ <td>557.19</td>
+
+ <td>4429.3</td>
+
+ <td>1201.16</td>
+
+ <td>9.56</td>
+
+ <td>2171.19</td>
+
+ <td>113.58</td>
+
+ <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+
+ </tr>
+
+ <tr>
+ <td style="display: flex">
+
+ <div style="display: flex">
+ acer
+ </div>
+ </td>
+
+ <td><div style="background-color:rgba(231, 138, 195, 1.0)"> </div></td>
+
+ <td>cron</td>
+
+ <td>1457.36</td>
+
+ <td>0.0</td>
+
+ <td>656.91</td>
+
+ <td>6433.38</td>
+
+ <td>1065.98</td>
+
+ <td>3.11</td>
+
+ <td>1959.22</td>
+
+ <td>82.94</td>
+
+ <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+
+ </tr>
+
+ <tr>
+ <td style="display: flex">
+
+ <div style="display: flex">
+ ppo2_mpi
+ </div>
+ </td>
+
+ <td><div style="background-color:rgba(166, 216, 84, 1.0)"> </div></td>
+
+ <td>cron</td>
+
+ <td>1417.92</td>
+
+ <td>207.47</td>
+
+ <td>459.89</td>
+
+ <td>7184.73</td>
+
+ <td>1383.38</td>
+
+ <td>13.9</td>
+
+ <td>594.45</td>
+
+ <td>81.61</td>
+
+ <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+
+ </tr>
+
+ <tr>
+ <td style="display: flex">
+
+ <div style="display: flex">
+ a2c
+ </div>
+ </td>
+
+ <td><div style="background-color:rgba(255, 217, 47, 1.0)"> </div></td>
+
+ <td>cron</td>
+
+ <td>717.73</td>
+
+ <td>0.0</td>
+
+ <td>463.06</td>
+
+ <td>2047.07</td>
+
+ <td>1150.66</td>
+
+ <td>1.0</td>
+
+ <td>1302.61</td>
+
+ <td>59.72</td>
+
+ <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+
+ </tr>
+
+ <tr>
+ <td style="display: flex">
+
+ <div style="display: flex">
+ trpo_mpi
+ </div>
+ </td>
+
+ <td><div style="background-color:rgba(229, 196, 148, 1.0)"> </div></td>
+
+ <td>cron</td>
+
+ <td>625.67</td>
+
+ <td>24.83</td>
+
+ <td>457.7</td>
+
+ <td>2486.18</td>
+
+ <td>710.07</td>
+
+ <td>2.82</td>
+
+ <td>683.11</td>
+
+ <td>14.98</td>
+
+ <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+
+ </tr>
+
+ </table>
+
+</form>
+ <div style="margin-top: 60px; text-align: center">
+ <h3>Learning Curves</h3>
+ X-axis: timesteps, Y-axis: reward (avg. over 6 runs)
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diff --git a/baselines/benchmarks_mujoco1M.htm b/baselines/benchmarks_mujoco1M.htm
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+ <meta name="author" content="">
+ <link rel="icon" href="https://cloud.githubusercontent.com/assets/306655/25257645/38a30806-25ee-11e7-82d5-eae86a9dfad0.png">
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+
+ <div class="benchmark-title">
+ <h1>Mujoco1M Comparison</h1>
+ </div>
+ <div style="text-align: center">
+<br />
+</div>
+
+ <table class="table sortable-data-table">
+ <thead>
+ <tr>
+ <th> bmrun________________________ </th>
+
+ <td> </td>
+
+ <td>user</td>
+
+ <td>mean</td>
+
+ <td>HalfCheetah</td>
+
+ <td>Hopper</td>
+
+ <td>InvertedPendulum</td>
+
+ <td>Swimmer</td>
+
+ <td>InvertedDoublePendulum</td>
+
+ <td>Reacher</td>
+
+ <td>Walker2d</td>
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+ <td>commit</td>
+
+ </tr>
+ </thead>
+
+ <tr>
+ <td style="display: flex">
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+ <div style="display: flex">
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+ </td>
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+ <td><div style="background-color:rgba(102, 194, 165, 1.0)"> </div></td>
+
+ <td>cron</td>
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+ <td>1896.01</td>
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+ <td>1289.7</td>
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+ <td>1912.9</td>
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+ <td>905.1</td>
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+ <td>94.96</td>
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+ <td>6731.63</td>
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+ <td>-4.82</td>
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+ <td>2342.63</td>
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+ <td>ea68f3b</td>
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+ <tr>
+ <td style="display: flex">
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+ <div style="display: flex">
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+ <td><div style="background-color:rgba(252, 141, 98, 1.0)"> </div></td>
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+ <td>1668.58</td>
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+ <td>2316.16</td>
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+ <td>809.43</td>
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+ <td>111.19</td>
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+ <td>7102.91</td>
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+ <td>-6.71</td>
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+ <td>3424.95</td>
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+ <td>ea68f3b</td>
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+ </tr>
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diff --git a/baselines/data/cartpole.gif b/baselines/data/cartpole.gif
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diff --git a/baselines/docs/viz/viz.ipynb b/baselines/docs/viz/viz.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f1741c1c7e3b026b88d64c3213fdbc421d8b0d26
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+++ b/baselines/docs/viz/viz.ipynb
@@ -0,0 +1,808 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Ynb-laSwmpac"
+ },
+ "source": [
+ "# Loading and visualizing results ([open in colab](https://colab.research.google.com/github/openai/baselines/blob/master/docs/viz/viz.ipynb))\n",
+ "In order to compare performance of algorithms, we often would like to visualize learning curves (reward as a function of time steps), or some other auxiliary information about learning aggregated into a plot. Baselines repo provides tools for doing so in several different ways, depending on the goal."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "yreoV7OClzYG"
+ },
+ "source": [
+ "## Preliminaries / TensorBoard\n",
+ "First, let us install baselines repo from github"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "r4Aul2Qujlg9"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install git+https://github.com/openai/baselines > ~/pip_install_baselines.log"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "1n7XAyVWniRp"
+ },
+ "source": [
+ "For all algorithms in baselines summary data is saved into a folder defined by logger. By default, a folder $TMPDIR/openai-<date>-<time> is used; you can see the location of logger directory at the beginning of the training in the message like this:\n",
+ "\n",
+ "Logging to /var/folders/mq/tgrn7bs17s1fnhlwt314b2fm0000gn/T/openai-2018-10-29-15-03-13-537078\n",
+ "The location can be changed by changing OPENAI_LOGDIR environment variable. For instance, "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 68
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 32433,
+ "status": "ok",
+ "timestamp": 1541626389411,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "xzqFBYiZjtUr",
+ "outputId": "92dc8be5-c18c-4399-b2d0-0f444b321a13"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 128, 'network': 'mlp'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "!OPENAI_LOGDIR=$HOME/logs/cartpole-ppo OPENAI_LOG_FORMAT=csv python -m baselines.run --alg=ppo2 --env=CartPole-v0 --num_timesteps=30000 --nsteps=128"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "WYwTBFMCn95w"
+ },
+ "source": [
+ "Notice also the usage of `OPENAI_LOG_FORMAT` environment variable - this allows us to suppress printing to stdout. \n",
+ "Permissible values for `OPENAI_LOG_FORMAT` environment variables are `stdout`, `log`, `csv`, and `tensorboard` (multiple values can be comma-separated). \n",
+ "The latter one (`tensorboard`) dumps values in tensorboard format, which allows for their viewing in [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard). TensorBoard provides nice visualization and in many cases is the easiest way to look at the summary of the results. However, if tensorboard visualization is not sufficient, read on...\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "QcWDWCfIojHy"
+ },
+ "source": [
+ "## Loading results and plotting using matplotlib\n",
+ "Bаselines provides helper functions to load the summaries of the results as pandas dataframes. \n",
+ "For instance, the following:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 71
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 361,
+ "status": "ok",
+ "timestamp": 1541626398698,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "cDdZjFR2kFG2",
+ "outputId": "09d91a7a-f851-459b-903a-f8c1d32ccb8e"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/dist-packages/baselines/bench/monitor.py:164: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
+ " df.headers = headers # HACK to preserve backwards compatibility\n"
+ ]
+ }
+ ],
+ "source": [
+ "from baselines.common import plot_util as pu\n",
+ "results = pu.load_results('~/logs/cartpole-ppo') "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "sj3jfhbBpGPq"
+ },
+ "source": [
+ "will search for all folders with baselines-compatible results in ~/logs/cartpole-ppo and subfolders and return a list of `Result` objects. Each `Result` object is a named tuple with the following fields:\n",
+ "\n",
+ " * dirname: str - name of the folder from which data was loaded\n",
+ " \n",
+ " * metadata: dict) - dictionary with various metadata (read from metadata.json file)\n",
+ "\n",
+ " * progress: pandas.DataFrame - tabular data saved by logger as a pandas dataframe. Available if csv is in logger formats.\n",
+ "\n",
+ " * monitor: pandas.DataFrame - raw episode data (length, episode reward, timestamp). Available if environment wrapped with Monitor wrapper\n",
+ " \n",
+ "Thus, a learning curve from a single run can be plotted as follows (note the cumulative sum trick to get convert lengths of the episodes into number of time steps taken so far)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 364
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 635,
+ "status": "ok",
+ "timestamp": 1541626401733,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "jEBxUzvVsNbP",
+ "outputId": "7b7016a5-6f94-4384-821d-a9e197211315"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7f4d4bba0518>]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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oydrz1ETerZjLWpp52341p2taDGF0GrJuXo1UTwathxxWEAwUCytbWLJkaqOk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6OqqgoA0Nrais7OztQ6mQRRIljKoarEm5u8lPscVgiTCc38hzsew+LZLTg4MIKb33MKvGWubOTq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\nkCFDhgwZDgH8/6BZS4beIGdHAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4d4bbe1b00>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "r = results[0]\n",
+ "plt.plot(np.cumsum(r.monitor.l), r.monitor.r)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "lLGJgLFjqLDB"
+ },
+ "source": [
+ "The raw learning curve from a single run can be very noisy. To smoothen it and analyze if any learning actually takes place, we can use `plot_util.smooth()` function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 364
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 514,
+ "status": "ok",
+ "timestamp": 1541626405684,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "Rz4vAnURuURX",
+ "outputId": "f5e38cd7-c63c-4b9c-aaa0-b24592b636ba"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7f4d4bb85550>]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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zZSNraVzBGwywmPmsOCdz05nlhhKFXG9KmVCeGt23bu7CJavbcW7Uh/t/fgSP\nPXM277GKuUr7R3w4dKpwSEsdo35ozyn88yOv53yeKIr43995CQDQ6pJcjStky+vcaPaJNl5Ga0i3\nnEl7UF6zujRJL0yyVcfWx2LvejbZSJUZVTbmy9zOc2W6ndXJUUfOTuFv/+1lPPz0aYiCCHX0RWoz\naU13O89F0OQwKx4eFl6oZocrTd8sr9eL1tbU1eS9996Lb37zm7j99tvR29uL7du3V2yBBLHYYeUx\nQKr5QyaCmB7nyofDasybcGVhJ6ACSSdKa8g8sWWbxYi/+sAW/P2fbUOz04wXjoyk1XHmu52Lh/9w\nGt/5xRsFZ7JqHSoQCKcyaplIpsQ3O0FMS8w3E5bxfPy8ZDl2t2V3opov5owYL/t76xnzrXR3KwbL\nQcjsuKaVzAYpU74I9rx6AbGEkPU78LTYEIwkEIzEIYgipv0RtKnKjpSYb60lXG3atAk//OEPlf+v\nXr0au3fvxsMPP4zPf/7zFVscQSx2BFFMq+sM57F8mfgWK9+xW4xZCVcs6cjlkCyRQuUWhRKu1Cz1\nOHH5Wg+CkQROXUi5zWdUn6XYEPNgOA4RwJ5XL+R9jlbxVXeFYiLZ5DCjrcmKc6O+rNyVeBnjAN2y\nWIWjCfAGDu4m/cXrL27eiN4OJz50/WoAKU+Hnh2uZuW9qnTCFWvdGSlzKhOr0W7N2OcLEwFwGb+D\nDlW50VwghkRSVJKtAOlCrLfTqVyQVQPqcEUQNYwvGEsTw3A+y1djv2S71YRwNKE8f3DcjwNverF8\niQtreiQ3abyAKDLBNGqo0b1U7uykdh17VVZ8MU8pa1X50pFR+DPKUUKROARBivkZOA5//cGLAUiW\nfS6mVeLbmnbSdcEfiqe59oEyLV+VCNgsxqJeiHLo8Thx359uU6ZWsYstPYfAK5avo7JuZyNvgIHj\nyp5HzGq01/e6sXppc5qbPHNoh0fOeH7h8Ijifm5XWb6eFhvu+5NtWNlN4ksQBFIuZyYC+WK+mW7I\nfLAuV8yCfvyFcwCA265ZqbjeklosXw2tIdcta4HNYsTB017FMk8rCUFhwWDx3FhCwDOHhpX7p30R\n/O/vvITf7h1AJJaExWzAllVtWNJqz2v5M8t39dJmbN+S6svMLJ2BjLhvOeLbqrIUC01D0hP299bV\n8g3E4LAa82a86wXHcbCY+bLnOsdly9dq5nHvRy7HZz5wsfJY5kXolRs7AUjxeFaD3bdkYSsiSHwJ\nooZhFhmbZJPvRKXV8mWWYTAcx6HTXrx+ZhJrljbjohWtSnJRoYQrVk+rZc6rkTfg8nUeTPuiOD4w\njV88fxYP/vdJ5fFCxpogZ7Kk2t/HAAAgAElEQVT2djphtxjx9GtDSh/mU0OziCUEHDw9iVg8qfRf\n5nkurwix5hfvv2ZlWr9mJr79GXFf9l6liK86RmqvYFtGNezvrWfM1x+O58yorwRWM49ofH4xX9ba\nU33Bk/kzaLKbsXZpM/yhOI72TwEANiwvfeCFnpD4EkQNwyxf1t0o39Qg9SSXQnTIPY0HxwN4o38a\nAHDLjpXgOE6xfPMlXMUTAp7cNwizyYAdGkYHAqnpP88dGsFvXj6f9lghV2k0loQISdCuu7QH/lAc\nB05K7uvBsYD8GfyYDURhkQfe81wB8ZVjze6MntN9S1zgIF1UqDOG4wkBRp4ryXVssxgVYa9kT2Q1\nfAVivsmkUNYYxHKwmvmy3c6ZF0hqS53PEatvdlogAjg1NIfeTqeSbb1QkPgSRImIoogLE4Gq9IGd\nnGOWbzHxZW7nwsdbJ/cafvPCLManpSxiFucyyUlU+cotXnxjFDP+KK6/dKnmE9fK7iYs9TiU8hs1\nhcRXPcx+2wapj8CxAeli4bw81UcUJevHYpJOYzzP5bUAWczXnVE+Y7MYsWllG86N+vGblwaU++OJ\n8gTIZqmu+HIci/nqd8xEUswpXpXAYirf7axYvvLfX+3RyPX9bFbFsPVs+1kuJL4EUSKHz0zhy/+5\nH7t/f6ri78XczmuXSclQ3tlIzudpjcWu6GqCyWjAm4MzGJsOoa3Jopy0jEqtY+6LildPjAMA3rFt\nmeb1cxyHay/pyRndLWSssdi2zWJUWkGeOD8DURRxfswP9TWGVV6/wZDb8o3EEjhxfgYWE6/Uyar5\nxHs2oL3ZisdfPKfEA2eDMWUQRSlYmRVepaERzDDXM+EqkRTSRkZWEquZRywhlOU2Z73OmdvZYk59\n95tz1Cir77NW6eKoECS+BFEiJ+W4595j4xV/L+9sGDYLj94OF0xGA0YmgzmfJwraLF+T0YDVPc0Y\n8gYx44+iU3ZDA6lax3ziG44lYTYZSh7/dtVFnTmtSK2Wr4HjsL7PjRl/FMfOTSMUTWDzqjbluYrb\n2WBAUhCzjrv/xIT8eO7NcdnN+KOdUunO2eE5BMJx+IIxZfhCKVjmWT5TKkqdr07aK8hTq4xVmrXM\nLvy0loypYZaviXk+VGvO1SCk2ZH63lbr4qIQJL4EUSKpYfCV/QGfujCL0akQVvU0w2Dg0NVmx+hU\nMGc3I5YkpWV27DrVmDu1+LKa1pHJYM5mA/GEoFgZpWC3mnDF+uwWtIWMNeZet8nCurFPSo75iext\nWNfboux/5r+Z+8NKZz50w+q878dqWgORuHKBU4742pj45gkP6I3elq9SSlalmK9ysVKG6zmuJFzl\niO86si8Q1YKcqz96tVn4FRBEncFmp+o1JD0fT7wklQGxWaPd7Q7EEgImVW3yAKmM5vu/OqZ5Tet6\nU+K7RCW+rHHGL184h5/94UzW62LxZNmJOO+8sle5zURNq+ULABtk8Z2YCcNhNeLqzV3KMbdtlBvq\n58n89Qel7lZ9nflLS1h2bzCcwIjcJ7ucDlXM7Vwty1eJ+ep0POb10FLHrQfs71uW5ZvhdlaTy/Jt\nUsV8q/X5CkHiSxAlwupgi3V5mg+nh2ZxfGAGFy13Y42cHLJEbqwwkSG+Z4dTHaRY159CrOxuUq78\nl+SwfAHg5WNjWa+TLN/yThlLPU48cM9O/MXNG/HJWzYB0Gb5sthch9umlFvdsmMlXHYzbt6+HF/5\n0214y8YlAFIXHpkJY2wYRFOBphHqEixm+Xa12/M+Px+3XbMSFjOPj7xtbcmvLQemIXqVGrGYebUs\nQ4s8caicjOfMhCs1uUIj6lKwaiWUFWLho84EUWewE10lr55ZWc0739Kn3Mfa4Z0ZmsOq7mYlo1bd\nMN5qKe4WNhl5rO5pwsnBWXTliPkCQG9H9jjAWEKAax7lGRzH4S0XLVHcwIWGAWRavhzH4W//+FIM\njgewUa7PNJt4LFOtM5/bmXXHKlS7yvoMByNxJdmrHMt3aYcT3/vf15b8unLhdO5wlajChaWalNu5\ndDc9s3xzJdHlSrhy2lN//1qI+ZL4EkSJaJkgNF9Y4wF183d2+4mXBnBhIoC73r8FADAmlwy9/9qV\neOumLk3H/9D1a9A/6kN7S2pAuzrO195izXpNPJHMaWWUipbyGHYyZm5cQEqMyjcuEFDVvGZZvnE4\nrMaC1hxvMMBmMSIQlprvu12WqpULzQeDzqVGrMa7WpavrWIx32zxVddsU8yXIOqEaCypWLyltFgs\nF6W9oeokoW4Ef+j0pHJ7fDoEjgPefkWv5phs3xIXdl7ak3afuq1k5sAhQRCRSIplu53VaEkSYmMP\nS2nTyFyJmeVGvmCsoMuZ4bAaMeULY8YfLSvZaiFgcqLXSEGWuFcty5BZvvnq1wsRy9ECdLXcnzxX\nwpWaajURKUTtX9oRxAIjCCL+1zefQ1+nC1/+kytU4lu5E1Q8o4wCSE3jAVJNNwBgbCaM9mbrvE8o\naiszc9xfOfNt86HFWjt+fga8gUOPR7sIpvocp48tDIbjmsTUaTMpTU0qMQ6wEujdZIPlDFS6rzOD\nhT0Gxvx4y0VLSnot63Clbq7xt398KRIaOnRV8sJZKwu/AoKocdjJnHVWUhKuKii+sRyWr5E34N6P\nXJ62plAkAV8wllYyVC4bl7vxZ+/eACA7aYlNkCmn1CgTZvnms9YmZ8M4P+bH+j53SY0uWJxSnXwU\nCCcgAmiyFz+OQxUT7i4j2WohKLaXpcKyji1VEt9VPc0w8gaclGcglwK7UFALrcloKBguYM/VkhtR\naUh8CaIImS5YJox6x3xfPDKKx1/ox8RMKBXPyoixrl7ajLYmq3LiYYPml+ggvhzH4fJ10hjAzEYb\nheJrJb8PCltrp4ek7O0tqkYaWsjV59gflJKtXBrdzox6cTsbdE64Ui6yqiS+ZpOU/Dc4EUAgHC/p\ntalsZ+1r/fs/3Ybbb1ij1I0vJOR2JogiZMYQWTKQvv10Bfz4qTeRSAp4QtVjOJd7zGwywB+STlQs\n2UoP8QWQd6xgrvhauRSL+c7JgtnelJ30VYhc4quUGWnI0lZbvl1143aW/tXruxiNSX/nalm+gFTD\nfXJwFifPz2BrjmYs+YiVMXmqs9WOt+v0W5kvZPkSRBHULr1AOI7BcWmqTiSe1M3iYIMa1i5rwZql\nzQWfazbxyolnbEoSXz3czkBKwDLdzvGM8W3zwVCkMYQvWLwuN+dxc2Q7p8RXg9tZ5eKu1ki9+ZKK\nn+tt+VZPGjb0SRnsJwZLcz3Hc7id64n6XDVBVBF1DPHgKa9iWY1Ph/DqyQld3oM1ytixpQufvHVz\nwedajAbE4gJEUVTczl06iS/HceANHBJCpuUrWxm6lBpJ/+aLUzLB1OIqVsP6EauPy7pbaalPZjXD\nenkRqgGnc2/nasd8AWB5lwscB1yQL2q1EktIHddKGftYS5D4EkQR1G7MfcelYQp/+d6LAKTik2oE\nQcTzh0fwgyeOKSUU4zMhnB/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M0XmIr0mpZ5VOKOrGG7XqPstsL6kkXFWg1CgpCIgnhAXv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\n1IoAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4d4bc1be48>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(np.cumsum(r.monitor.l), pu.smooth(r.monitor.r, radius=10))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "_e-NRHW8qoMF"
+ },
+ "source": [
+ "Similar curve can be obtained by using logger summaries (instead of raw episode data in monitor.csv):\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 364
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 619,
+ "status": "ok",
+ "timestamp": 1541626409332,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "E2PawoKEua1N",
+ "outputId": "02c4b58e-b665-4534-d02c-24c5862e473f"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7f4d4bc35240>]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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BlK52WL04Dh4uXF6SiIyPAUxWQxAE7DyRjz2nO8N3TVIc13YmItEwgMkqCIKA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\nAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4d513dcbe0>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(r.progress.total_timesteps, r.progress.eprewmean)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "2iI5DRKArEVl"
+ },
+ "source": [
+ "Note, however, that raw episode data is stored by the Monitor wrapper, and hence looks similar for all algorithms, whereas progress data is handled by the algorithm itself, and hence can vary (column names, type of data available) between algorithms."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "ETICR8zsrKg2"
+ },
+ "source": [
+ "## Plotting: many curves\n",
+ "While the loading and plotting functions described above in principle give you access to any slice of the training summaries, sometimes it is necessary to plot and compare many training runs (multiple algorithms, multiple seeds for random number generator), and usage of the functions above can get tedious and messy. For that case, `baselines.common.plot_util` provides convenience function plot_results that handles multiple `Result` objects that need to be routed in multiple plots. Consider the following bash snippet that runs ppo2 with cartpole with 6 different seeds for 30k time steps, first with rollout batch size 32, and then with batch size 128 (note that the next cell will take a little while to run):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 629
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 470993,
+ "status": "ok",
+ "timestamp": 1541626885128,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "OLEYIE95ue2e",
+ "outputId": "d1f2760b-1433-4b73-c5ce-ceace66e07cc"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 32, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 32, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 32, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 32, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 32, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 32, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 128, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 128, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 128, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 128, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 128, 'network': 'mlp'}\n",
+ "env_type: classic_control\n",
+ "Training ppo2 on classic_control:CartPole-v0 with arguments \n",
+ "{'nsteps': 128, 'network': 'mlp'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "!for seed in $(seq 0 5); do OPENAI_LOG_FORMAT=csv OPENAI_LOGDIR=$HOME/logs/cartpole-ppo-hp/b32-$seed python -m baselines.run --alg=ppo2 --env=CartPole-v0 --num_timesteps=3e4 --seed=$seed --nsteps=32; done\n",
+ "!for seed in $(seq 0 5); do OPENAI_LOG_FORMAT=csv OPENAI_LOGDIR=$HOME/logs/cartpole-ppo-hp/b128-$seed python -m baselines.run --alg=ppo2 --env=CartPole-v0 --num_timesteps=3e4 --seed=$seed --nsteps=128; done"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "XzG8DDNwrwbG"
+ },
+ "source": [
+ "The results of 12 runs from the cell above can be loaded just as before, via (we discard first result that is actually from the very first run in the previous section):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 88
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 319,
+ "status": "ok",
+ "timestamp": 1541629440197,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "cNKemLHPv03E",
+ "outputId": "8ff4813e-23f3-4696-a531-6ef3bd12c569"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "12\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/dist-packages/baselines/bench/monitor.py:164: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
+ " df.headers = headers # HACK to preserve backwards compatibility\n"
+ ]
+ }
+ ],
+ "source": [
+ "results = pu.load_results('~/logs/cartpole-ppo-hp'); print(len(results))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "xy388w_tssIL"
+ },
+ "source": [
+ "But how do we plot all 12 of them in a sensible manner? `baselines.common.plot_util` module provides plot_results function to do just that:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 937
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 858,
+ "status": "ok",
+ "timestamp": 1541629448579,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "1yemaAkKweB2",
+ "outputId": "612d1da5-e2a1-4836-b223-aebfaa3feac6"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(<matplotlib.figure.Figure at 0x7f4d4bc1bc88>,\n",
+ " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4d4bad9358>],\n",
+ " [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4d4bab7f28>]],\n",
+ " dtype=object))"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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Ec1hYyXaJzkPq+rO+q/8Ws7Z8gLu/+zM9RgxZwBlALBvtchKALMv4ZOssFLgK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\nkBiGYZisgAWJYRiGyQpYkBiGYZis4P8DMxRrQOAKNv8AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4d4bc1bc88>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pu.plot_results(results)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "uUNu5fEfsvot"
+ },
+ "source": [
+ "The results are split into two groups based on batch size and are plotted on a separate graph. More specifically, by default plot_results considers digits after dash at the end of the directory name to be seed id and groups the runs that differ only by those together.\n",
+ "\n",
+ "Showing all seeds on the same plot may be somewhat hard to comprehend and analyse. We can instead average over all seeds via the following command:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 937
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 848,
+ "status": "ok",
+ "timestamp": 1541629456405,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "7d_aeRYXacFP",
+ "outputId": "22554c64-b5a5-4892-81d0-8a7d0d3569a4"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(<matplotlib.figure.Figure at 0x7f4d4bcd8240>,\n",
+ " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4d4b9f55c0>],\n",
+ " [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4d4b9bda20>]],\n",
+ " dtype=object))"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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ePvM4j3IAy7GIZ/NTeFUnRDa7kFVGyV+MZ1KzZLMGJ3NnuKj3ImYSsbLfSdLJ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Ozrqewvp5eK9L0zYvIKgWDlGIrRu2L+KAtI2QzdJiMTpKRngDA/TNlgubulBHQZZiuruj6dgA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\niCgl/D/3HGz6UtSjSgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4d4bcd8240>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pu.plot_results(results, average_group=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "iA89jxdgtElg"
+ },
+ "source": [
+ "The lighter shade shows the standard deviation of data, and darker shade - error in estimate of the mean (that is, standard deviation divided by square root of number of seeds). Note that averaging over seeds requires resampling to a common grid, which, in turn, requires smoothing (using language of signal processing, we need to do low-pass filtering before resampling to avoid aliasing effects). You can change the amount of smoothing by adjusting `resample` and `smooth_step` arguments to achieve desired smoothing effect See the docstring of plot_util function for more info.\n",
+ "\n",
+ "To plot both groups on the same graph, we can use the following:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 488
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 581,
+ "status": "ok",
+ "timestamp": 1541629463814,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "_5IMVF3EGBMD",
+ "outputId": "b29e81b6-8a57-4007-fe31-86652b7ee1e5"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(<matplotlib.figure.Figure at 0x7f4d4b919cc0>,\n",
+ " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4d4b95f198>]],\n",
+ " dtype=object))"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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o0LeukRB1+rIpRvrXvo9JTyUdLq2xZJfYP/04EHlc3d0Z5oNJdnTvQC8V8ZUo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pxirb4WKYqv19e43jIFTNsZMrEMx7OnxM7chaMjRxltqTOEpn9vvlLgm/8lxsWLBnNzio0Ng/Pn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\nckGhfxMIU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wHDDGkfotHoZhutrytefNHm+nUT31esrxu89prFiy/akUU1yECibYudwRtv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\nRK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4d4b919cc0>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pu.plot_results(results, average_group=True, split_fn=lambda _: '')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "ABZmMmCmtQ1X"
+ },
+ "source": [
+ "Option `split_fn=labmda _:''` effectively disables splitting, so that all curves end up on the same panel.\n",
+ "\n",
+ "Now, with many groups the overlapping shaded regions may start looking messy. We can disable either light shaded region (corresponding to standard deviation of the curves in the group) or darker shaded region (corresponding to the error in mean estimate) by using `shaded_std=False` or `shaded_err=False` options respectively. For instance,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 488
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 546,
+ "status": "ok",
+ "timestamp": 1541629471846,
+ "user": {
+ "displayName": "Peter Zhokhov",
+ "photoUrl": "",
+ "userId": "10254602425711636265"
+ },
+ "user_tz": 480
+ },
+ "id": "x4rVG6RGI31B",
+ "outputId": "a036e51d-b6c6-4855-ce5a-a42c149a59ee"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(<matplotlib.figure.Figure at 0x7f4d4b8577b8>,\n",
+ " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4d4b8bc4e0>]],\n",
+ " dtype=object))"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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we8O7ORYfInsctbCcVmBveDeaoXFO+3nW8VJ0M5ocMd0c9DzNziCaoVnCM5dG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\nZ8ECywAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4d4b8577b8>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pu.plot_results(results, average_group=True, split_fn=lambda _: '', shaded_std=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "YMA86VtfI8d5"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "baselines_viz.ipynb",
+ "provenance": [],
+ "version": "0.3.2"
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/baselines/setup.cfg b/baselines/setup.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..0cd564a86e6ffa4a29df2e56d44205880d21c9e2
--- /dev/null
+++ b/baselines/setup.cfg
@@ -0,0 +1,6 @@
+[flake8]
+select = F,E999,W291,W293
+exclude =
+ .git,
+ __pycache__,
+ baselines/ppo1,
diff --git a/baselines/setup.py b/baselines/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..99a1eff68300c8847ec86d6f18401b7c6aad1b73
--- /dev/null
+++ b/baselines/setup.py
@@ -0,0 +1,62 @@
+import re
+from setuptools import setup, find_packages
+import sys
+
+if sys.version_info.major != 3:
+ print('This Python is only compatible with Python 3, but you are running '
+ 'Python {}. The installation will likely fail.'.format(sys.version_info.major))
+
+
+extras = {
+ 'test': [
+ 'filelock',
+ 'pytest',
+ 'pytest-forked',
+ 'atari-py~=0.2.0'
+ ],
+ 'bullet': [
+ 'pybullet'
+ ],
+ 'mpi': [
+ 'mpi4py'
+ ]
+}
+
+all_deps = []
+for group_name in extras:
+ all_deps += extras[group_name]
+
+extras['all'] = all_deps
+
+setup(name='baselines',
+ packages=[package for package in find_packages()
+ if package.startswith('baselines')],
+ install_requires=[
+ 'gym<=0.14',
+ 'scipy',
+ 'tqdm',
+ 'joblib',
+ 'dill',
+ 'progressbar2',
+ 'cloudpickle',
+ 'click',
+ 'opencv-python'
+ ],
+ extras_require=extras,
+ description='OpenAI baselines: high quality implementations of reinforcement learning algorithms',
+ author='OpenAI',
+ url='https://github.com/openai/baselines',
+ author_email='gym@openai.com',
+ version='0.1.5')
+
+# ensure there is some tensorflow build with version above 2.0
+import pkg_resources
+tf_pkg = None
+for tf_pkg_name in ['tensorflow', 'tensorflow-gpu', 'tf-nightly', 'tf-nightly-gpu']:
+ try:
+ tf_pkg = pkg_resources.get_distribution(tf_pkg_name)
+ except pkg_resources.DistributionNotFound:
+ pass
+assert tf_pkg is not None, 'TensorFlow needed, of version above 2.0'
+from distutils.version import LooseVersion
+assert LooseVersion(re.sub(r'-?rc\d+$', '', tf_pkg.version)) >= LooseVersion('2.0.0')
\ No newline at end of file