starry-sky6688/MARL-Algorithms

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1760 stars · 302 forks observed · 2026-08-28

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

PyTorch implementations of classic multi-agent reinforcement learning algorithms (IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, G2ANet) evaluated on SMAC, the StarCraft II micromanagement benchmark. It is research code for training and evaluating cooperative MARL algorithms.

Use cases

• implement qmix multi-agent reinforcement learning
• benchmark marl algorithms on smac starcraft ii
• learn value decomposition methods like vdn and qmix
• reproduce multi-agent rl research paper results
• train cooperative multi-agent policies in pytorch
• compare communication-based marl algorithms like commnet and g2anet

When to choose

• you want ready PyTorch implementations of many classic MARL algorithms in one codebase
• you need a benchmark on SMAC/StarCraft II micromanagement scenarios
• you are studying or reproducing cooperative multi-agent RL papers

When to avoid

• you need a maintained, production-grade MARL framework with a stable API
• you require a permissive license - the repo has no license
• you want environments other than StarCraft II/SMAC
• you need the latest SOTA MARL algorithms - the TODO list is unfinished

Facets

library · maturity maintenance

reinforcement-learning machine-learning simulation reinforcement-learning machine-learning python windows multi-agent-reinforcement-learning marl starcraft-ii smac pytorch qmix value-decomposition research-code game-development linux macos

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