AmineAndam04/cleanmarl

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

Reinforcement Learning algorithms and use-cases, including DQN, PG, A3C, PPO etc. and RLHF, AlphaZero implementations. Designed for clarity, ease of use, and educational purposes.

A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

Projects from basic algorithms to MARL. Implements MADDPG,MATD3,MA/HAPPO in Predator-Prey pursuit games with PettingZoo MPE environments.

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

Multi-Agent Reinforcement Learning with JAX

MinRL provides clean, minimal implementations of fundamental reinforcement learning algorithms in a customizable GridWorld environment. The project focuses on educational clarity and implementation simplicity while maintaining production-quality code standards.

A suite of test scenarios for multi-agent reinforcement learning.