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• W3099689767 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." @default.
• W3099689767 created "2020-11-23" @default.
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• W3099689767 date "2020-11-18" @default.
• W3099689767 modified "2023-09-27" @default.
• W3099689767 title "Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?" @default.
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