oxwhirl/pymarl

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2216 stars · 411 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyMARL is WhiRL's PyTorch framework for deep multi-agent reinforcement learning, implementing algorithms such as QMIX, COMA, VDN, IQL, and QTRAN. It uses the SMAC StarCraft II environment for cooperative multi-agent experiments and supports Docker-based setup, model saving/loading, and replay generation.

Use cases

• run multi-agent reinforcement learning experiments
• reproduce QMIX results on SMAC
• train cooperative multi-agent policies in StarCraft II
• compare value-decomposition MARL algorithms
• benchmark deep RL algorithms for multi-agent settings
• load and evaluate pretrained MARL checkpoints
• watch StarCraft II replays of trained agents

When to choose

• you need reference implementations of classic MARL algorithms like QMIX or COMA
• your research uses the SMAC StarCraft II benchmark
• you want a PyTorch codebase for multi-agent RL experiments

When to avoid

• you need a maintained framework with recent updates or new algorithm support
• you want environments other than StarCraft II without extra integration work
• you need production deployment rather than research experimentation

Facets

framework · maturity maintenance

reinforcement-learning machine-learning agent-framework reinforcement-learning machine-learning artificial-intelligence gaming-tools python multi-agent-reinforcement-learning marl qmix coma vdn qtran smac starcraft-ii pytorch research linux docker gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="oxwhirl/pymarl")