mhyrzt/Simple-MADRL-Chess
MADRL project solving chess environment using PPO with two different methods: 2 agents/networks and a single agent/network.
This project helps machine learning researchers explore different strategies for training AI agents to play chess. It takes in configurations for Proximal Policy Optimization (PPO) algorithms and outputs trained chess-playing agents, along with plots visualizing their learning progress. A machine learning researcher focused on multi-agent reinforcement learning would find this useful for experimentation.
No commits in the last 6 months.
Use this if you are a machine learning researcher interested in comparing single-agent vs. multi-agent PPO for game AI development.
Not ideal if you are looking for a fully robust chess engine or a general-purpose reinforcement learning library for domains other than chess.
Stars
19
Forks
3
Language
Python
License
MIT
Category
Last pushed
Apr 01, 2023
Commits (30d)
0
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