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Most recently developed approaches to cooperative multi-agent reinforcementlearning 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, IndependentPPO (IPPO), a form of independent learning in which each agent simply estimatesits local value function, can perform just as well as or better thanstate-of-the-art joint learning approaches on popular multi-agent benchmarksuite SMAC with little hyperparameter tuning. We also compare IPPO to severalvariants; the results suggest that IPPO's strong performance may be due to itsrobustness to some forms of environment non-stationarity.

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