SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning

Understand

There is a lack of standard benchmarks for Multi-Agent Reinforcement Learning (MARL) algorithms.

• The Starcraft Multi-Agent Challenge (SMAC) has been widely used in MARL research, but is built on top of a heavy, closed-source computer game, StarCraft II.
• Thus, SMAC is computationally expensive and requires knowledge and the use of proprietary tools specific to the game for any meaningful alteration or contribution to the environment.
• We introduce SMAClite -- a challenge based on SMAC that is both decoupled from Starcraft II and open-source, along with a framework which makes it possible to create new content for SMAClite without any special knowledge.