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Learning Optimal Policies With Local Observations for Cooperative Multiagent Reinforcement Learning
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Summary
Learning Optimal Policies With Local Observations for Cooperative Multiagent Reinforcement Learning proposes unified MARL (UMARL), a weighted value function factorization method that combines exploitation and exploration in one framework. The authors prove a latent state can guarantee optimal individual and global policies and can be approximately obtained from local observations. UMARL uses an agent representation network, individual weighting networks and a latent state regularizer, and outperforms 12 state-of-the-art methods on m-step matrix game, level-based foraging, StarCraft II and Google research football.