I use Deep Reinforcement Learning (DRL) to train computer agents to walk, hop, or even play an Atari 2000 game!
The best performing agent after 4 hours of training. Yeah, it's difficult to learn to walk like humans.
Our trained agent (the green player) learned to win consistently in Pong!
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!
A simulated RTS game. The white, green block and circle are bases, resources and workers. The purpose of the game is to gather resources, build an army and destroy enemy forces.
A trained agent learning to hop with a single leg
You can easily use my DRL library CleanRL to train the agents to play all of the games above.
Feel free to check out my projects and papers below. If you want to get in touch with me, email me at [email protected]
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🔧 My Projects
Copy of Click on any of them for details
(2019) CleanRL
PythonReinforcement LearningGame AIPytorchGame
DRL library to train computer agents to play various games
(2019) Gym-MicroRTS
PythonReinforcement LearningGame AI
Simulated game environment to experiment DRL algorithms
(2018) Portwarden
GoBitwarden
Utility to backup password manager Bitwarden
(2018) Add Disqus to Jupyter Notebook
PythonJupyter Notebook
Python pakcage that enable comments for your jupyter notebooks
(2017) StreetTraffic
PythonData AnaylysisVisualization
Server package to collect traffic data and plan optimal travel time
(2017) Training StarCraft II Agents
PythonReinforcement LearningGame AI
Project to train agents to learn collecting minerals