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SMAC (StarCraft II Multi-Agent Challenge) is a benchmark environment for cooperative multi-agent reinforcement learning (MARL), based on real-time strategy (RTS) game scenarios in StarCraft II. It allows researchers to test algorithms where multiple units (agents) must collaborate to win battles against built-in game AI opponents. SMAC provides a controlled testbed for studying decentralized execution and centralized training paradigms in MARL.

Features

Focuses on decentralized multi-agent cooperation challenges
Provides a variety of tactical combat scenarios in StarCraft II
Supports partial observability and limited communication among agents
Integrates with PyMARL and other MARL libraries for training
Includes a standard benchmark for evaluating MARL algorithms
Offers tools for measuring performance and analyzing agent coordination

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License

MIT License

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Additional Project Details

Programming Language

Python

Related Categories

Python Reinforcement Learning Frameworks

Registered

2025-03-13

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