oxwhirl/smac resource

Health v2 · maintenance only

Adoption not part of the score

1366 stars · 242 forks observed · 2026-08-28

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

SMAC is a benchmark environment for cooperative multi-agent reinforcement learning built on StarCraft II via Blizzard's SC2 ML API and DeepMind's PySC2. It provides decentralised micromanagement scenarios where each game unit is controlled by an individual RL agent.

Use cases

• benchmark cooperative multi-agent reinforcement learning algorithms
• train decentralized RL agents in StarCraft II micromanagement scenarios
• evaluate algorithms like QMIX and COMA
• research environment for MARL papers
• compare MARL methods on standardized scenarios

When to choose

• you need a standard benchmark for cooperative multi-agent RL
• you want decentralized control scenarios with discrete actions and observations
• you are reproducing MARL research like QMIX or COMA

When to avoid

• you need single-agent RL environments
• you cannot install StarCraft II or the SC2 ML API
• you want the newest scenario generation - use SMACv2 instead

Facets

dataset · maturity maintenance

reinforcement-learning simulation benchmarking machine-learning reinforcement-learning artificial-intelligence windows python starcraft-ii multi-agent-reinforcement-learning marl benchmark pysc2 decentralized-micromanagement qmix coma game-development research linux macos

Member repositories

For agents

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