Denys88/rl_games
RL implementations
This project helps robotics engineers and researchers train robots and intelligent agents to perform complex tasks using reinforcement learning. You can input simulated environments or real-world robotic data and get optimized control policies for dexterous manipulation, locomotion, or multi-agent coordination. It's ideal for those developing AI for robotics, autonomous systems, or game AI.
1,310 stars. Actively maintained with 3 commits in the last 30 days. Available on PyPI.
Use this if you are developing AI that needs to learn behaviors through trial and error, such as teaching a robotic arm to grasp objects or training agents to navigate complex environments.
Not ideal if you need a simple, low-overhead solution for basic machine learning tasks or if you are not working with robotic simulations or complex agent training.
Stars
1,310
Forks
205
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 01, 2026
Commits (30d)
3
Dependencies
9
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