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. We conduct experiments to show that SMAClite is equivalent to SMAC, by training MARL algorithms on SMAClite and reproducing SMAC results. We then show that SMAClite outperforms SMAC in both runtime speed and memory.

翻译：多智能体强化学习算法缺乏标准基准测试。星际争霸多智能体挑战（SMAC）虽广泛应用于多智能体强化学习研究，但其基于庞大且闭源的电脑游戏《星际争霸II》构建，导致计算成本高昂，且任何对环境的有意义修改或贡献都需要掌握该游戏特有的专有工具知识。我们提出SMAClite——一种脱胎于SMAC但脱离《星际争霸II》的开源挑战框架，配合该框架无需任何专业知识即可为SMAClite创建新内容。通过在SMAClite上训练多智能体强化学习算法并复现SMAC实验结果，我们证明SMAClite与SMAC等价。实验进一步表明，SMAClite在运行速度和内存占用方面均优于SMAC。