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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
Project Samples
Categories
Reinforcement Learning FrameworksLicense
MIT LicenseFollow SMAC
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