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

• 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

• A Closer Look at Invalid Action Masking in Policy Gradient Algorithms, Huang, S., Ontañón, S., Preprint.

• Comparing Observation and Action Representations for Reinforcement Learning in µRTS, Huang, S., Ontañón, S., AIIDE Workshop on Artificial Intelligence for Strategy Games, October 2019