opendilab/LightZero

Monte Carlo tree search in JAX

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

A library of reinforcement learning components and agents

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

bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent

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

Multi-Agent Reinforcement Learning with JAX

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