PettingZoo — Multi-Agent Reinforcement Learning

PettingZoo — Multi-Agent Reinforcement Learning

PettingZoo is the multi-agent counterpart to Gymnasium, providing a standard API for environments where multiple agents interact simultaneously. It covers cooperative, competitive, and mixed-motive scenarios — from classic board games (Chess, Go, Connect Four) to Atari multiplayer games (Pong, Surround) and particle physics environments — making it the standard benchmark suite for multi-agent RL research and game AI development.

Key Features

• 50+ multi-agent environments across 5 families: Atari, Classic, MPE, SISL, Butterfly
• AECEnv (turn-based) and ParallelEnv (simultaneous-action) APIs
• Compatible with RLlib, Stable-Baselines3 (via SuperSuit wrappers), and CleanRL
• Parallel environment vectorization for high-throughput training
• Standardized agent observation and action spaces across all environments

Quick Start

pip install pettingzoo[classic]

from pettingzoo.classic import chess_v6

env = chess_v6.env(render_mode="human")
env.reset(seed=42)
for agent in env.agent_iter():
    observation, reward, termination, truncation, info = env.last()
    if termination or truncation:
        action = None
    else:
        action = env.action_space(agent).sample()
    env.step(action)
env.close()

npx ai-supply add pettingzoo-multiagent-rl

Curated mirror of the open-source PettingZoo (MIT). Get it from the source.