![]() Please refer to the accompanying paper and blogpost for the outline of our motivation for using SMAC as a testbed for MARL research and the initial experimental results. Unlike the PySC2, SMAC concentrates on decentralised micromanagement scenarios, where each unit of the game is controlled by an individual RL agent. SMAC makes use of Blizzard's StarCraft II Machine Learning API and DeepMind's PySC2 to provide a convenient interface for autonomous agents to interact with StarCraft II, getting observations and performing actions. SMAC is WhiRL's environment for research in the field of collaborative multi-agent reinforcement learning (MARL) based on Blizzard's StarCraft II RTS game. The results in SMAC () use SC2.4.2 not SC2.4.10. ![]() Performance is *not* always comparable between versions. Please pay attention to the version of SC2 you are using for your experiments.
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