Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

Christian Schroeder de Witt; Tarun Gupta; Denys Makoviichuk; Viktor Makoviychuk; Philip H. S. Torr; Mingfei Sun; Shimon Whiteson

arXiv · 2011.09533

Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

Christian Schroeder de Witt·Tarun Gupta·Denys Makoviichuk·Viktor Makoviychuk·Philip H. S. Torr·Mingfei Sun·Shimon Whiteson

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Abstract

Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function. In this paper, we demonstrate that, despite its various theoretical shortcomings, Independent PPO (IPPO), a form of independent learning in which each agent simply estimates its local value function, can perform just as well as or better than state-of-the-art joint learning approaches on popular multi-agent benchmark suite SMAC with little hyperparameter tuning. We also compare IPPO to several variants; the results suggest that IPPO's strong performance may be due to its robustness to some forms of environment non-stationarity.

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Christian Schroeder de Witt, Tarun Gupta, Denys Makoviichuk, Viktor Makoviychuk, Philip H. S. Torr, Mingfei Sun, Shimon Whiteson. 2020-11-18. Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?. https://arxiv.org/abs/2011.09533

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