Download
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
Other Useful Business Software
Gen AI apps are built with MongoDB Atlas
MongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of SMAC!