Made with CleanRL

CleanRL has become an increasingly popular deep reinforcement learning library, especially among practitioners who prefer more customizable code. Since its debut in July 2019, CleanRL has supported many open source projects and publications. Below are some highlight projects and publications made with CleanRL.

Feel free to edit this list if your project or paper has used CleanRL.

Publications

The 37 Implementation Details of Proximal Policy Optimization, Huang, S., Dossa, R., Raffin, A., Kanervisto, A., & Wang, W. (2022). International Conference on Learning Representations 2022 Blog Post Track
A Closer Look at Invalid Action Masking in Policy Gradient Algorithms, Huang, S., Ontañón, S., (2022). The International FLAIRS Conference Proceedings, 35.
Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari Schmidt, D., & Schmied, T. (2021). Deep Reinforcement Learning Workshop at the 35th Conference on Neural Information Processing Systems
An Empirical Investigation of Early Stopping Optimizations in Proximal Policy Optimization, Dossa, R., Huang, S., Ontañón, S., Matsubara, T., IEEE Access, 2021
Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning, Huang, S., Ontañón, S., Bamford, C., Grela, L., IEEE Conference on Games 2021
Measuring Generalization of Deep Reinforcement Learning Applied to Real-time Strategy Games, Huang, S., Ontañón, S., AAAI 2021 Reinforcement Learning in Games Workshop
Griddly: A platform for AI research in games, Bamford, C., Huang, S., Lucas, S., AAAI 2021 Reinforcement Learning in Games Workshop
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games, Huang, S., Ontañón, S., AIIDE Workshop on Artificial Intelligence for Strategy Games, October 2020
Comparing Observation and Action Representations for Reinforcement Learning in µRTS, Huang, S., Ontañón, S., AIIDE Workshop on Artificial Intelligence for Strategy Games, October 2019