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 Reinforcement Learning


Near-OptimalReinforcementLearningwithSelf-Play

Neural Information Processing Systems

This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games. We focus on self-play algorithms which learn theoptimal policy by playing againstitself without any direct supervision.