Our trained agent learned to play breakout!!
My bot learns to play volleyball through self-play
My more recent work focuses on scaling DRL to Real-time Strategy (RTS) games, in collaboration with my advisor Santiago Ontañón.
Let's safely land our shuttle to the lunar surface. Gotta be steady and precise!
Have some fun with a car racing game!
A simulated RTS game. The white, green block and circles are bases, resources, and workers. The purpose of the game is to gather resources, build an army and destroy enemy forces.
You can easily use my DRL library CleanRL to train the agents to play all of the games above.
Feel free to get in touch with me at [email protected] 🎉.
🔧 My Projects
📖 My Publications
Huang, S., Reproducible and Efficient Deep Reinforcement Learning, 2023.
Huang, S., Dossa, R., Ye, C., Braga, J., “CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms”, Journal of Machine Learning Research, 2022
Dossa, R., Huang, S., Ontañón, S., Matsubara, T., “An Empirical Investigation of Early Stopping Optimizations in Proximal Policy Optimization”, IEEE Access, 2021
Weng, J., Lin, M., Huang, S., Liu, B., Makoviichuk, D., Makoviychuk, V., Liu, Z., Song, Y., Luo, T., Jiang, Y. and Xu, Z., 2022. “EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine”, NeurIPS 2022.
Huang, S.,, Dossa, R., Raffin, A., Kanervisto, A., Wang, W. “The 37 Implementation Details of Proximal Policy Optimization”. ICLR Blog Post Track, 2022
Huang, S., Ontañón, S., “A Closer Look at Invalid Action Masking in Policy Gradient Algorithms", FLAIRS-35, 2022
Compton, R., Valmianski, I., Deng, L., Huang, C., Katariya, N., Amatriain, X., Kannan, A. “MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System.” Machine Learning for Health, 2021.
Huang, S., Ontañón, S., S., Bamford, C., Grela, L., ‘’Gym-µRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning’’, IEEE Conference on Games 2021
Huang, S., Healy, C., “StreetTraffic: a Library for Traffic Flow Data Collection and Analysis”, ACMSE 2018 Conference, March 2018
Workshop / Preprints:
Huang, S., Kanervisto, A., Raffin, A., Wang, W., Ontanon, S., Dossa, R.F. A2C is a special case of PPO. preprint, 2022
Huang, S., Ontañón, S., “Measuring Generalization of Deep Reinforcement Learning Applied to Real-time Strategy Games”, AAAI 2021 Reinforcement Learning in Games Workshop
Bamford, C., Huang, S., Lucas, S., “Griddly: A platform for AI research in games.", AAAI 2021 Reinforcement Learning in Games Workshop
Huang, S., Ontañón, S., "Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games", AIIDE Workshop on Artificial Intelligence for Strategy Games, October 2020
Huang, S., Ontañón, S., “Comparing Observation and Action Representations for Reinforcement Learning in µRTS”, AIIDE Workshop on Artificial Intelligence for Strategy Games, October 2019