update

README.md

 # AWARE
 
-AWARE is an extensible framework for deploying and managing RL(reinforcement learning)-based agents in production systems.
+AWARE is an extensible framework for deploying and managing RL (reinforcement learning)-based agents in production systems.
 AWARE provides (1) fast adaptation with meta-learning, (2) reliable RL exploration with bootstrapping, (3) timely retraining by continuous monitoring.
 
 ## Requirements
@@ -46,7 +46,6 @@ aware/
 ```
 
 ## Testing
-### Functionality
 
 Start training with meta-learner, with model checkpoints saved to `testing/checkpoints/`.
 
@@ -79,6 +78,21 @@ The number of episodes is set to 500 for demonstration purpose.
 
 For running AWARE (training and policy-serving) with application `deployment` managed by RL-based MPA in Kubernetes cluster, follow `multidimensional-pod-autoscaler/README.md` to deploy MPA and `rl-controller/README.md` for training and customizing for application deployments from scratch.
 
+## Reference
+
+```
+@inproceedings {qiu2023aware,
+  author = {Qiu, Haoran and Mao, Weichao and Wang, Chen and Franke, Hubertus and Kalbarczyk, Zbigniew T. and Ba\c{s}ar, Tamer and Iyer, Ravishankar K.},
+  title = {{AWARE}: Automate Workload Autoscaling with Reinforcement Learning in Production Cloud Systems},
+  booktitle = {2023 USENIX Annual Technical Conference (USENIX ATC 23)},
+  year = {2023},
+  address = {Boston, MA},
+  pages = {1--17},
+  publisher = {USENIX Association},
+  month = jul,
+}
+```
+
 ## Contact
 
 Haoran Qiu (haoranq4@illinois.edu)