: This package includes the Python code of the EDO-CS algorithm  for finding a set of policies having both high rewards and diverse behaviors in reinforcement learning. In each iteration, the policies are divided into several clusters based on their behaviors, and a high-quality policy is selected from each cluster for reproduction. EDO-CS also adaptively balances the importance between quality and diversity in the reproduction process. Experiments on continuous MuJoCo locomotion tasks from the OpenAI Gym library , show the superior performance of EDO-CS. README files are included in the package, showing how to use the code.
:  Yutong Wang, Ke Xue, and Chao Qian. Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning. In: Proceedings of the 10th International Conference on Learning Representations (ICLR'22), Virtual, 2022.
 Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. OpenAI Gym. CoRR abs/1606.01540, 2016.
: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Dr. Chao Qian (firstname.lastname@example.org).
: The package was developed with Python.
: This package was developed by Ms. Yutong Wang (email@example.com). For any problem concerning the code, please feel free to contact Ms. Wang.