sotetsuk/pgx
♟️ Vectorized RL game environments in JAX
This project offers a collection of high-speed, parallel game simulators for researchers and developers working on reinforcement learning (RL) in discrete state spaces. It takes game rules and parameters as input, and outputs simulated game states and outcomes, enabling the efficient training and evaluation of AI agents. Anyone developing or experimenting with AI that learns to play board games like Chess, Go, or Backgammon would find this useful.
593 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to rapidly simulate many instances of board games for training reinforcement learning agents, leveraging GPU acceleration for speed.
Not ideal if you are looking for physics-based simulations or continuous control tasks.
Stars
593
Forks
44
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 06, 2025
Commits (30d)
0
Dependencies
3
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