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Provable Multiagent RL in Large State Space

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Saturday, October 16, 2021 3:00pm to 4:15am

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Saturday, October 16, 2021 3:00pm to 4:15am

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Multiagent reinforcement learning systems have recently achieved significant success in many AI challenges. Two crucial components that contribute to these successes are function approximation and self-play. Function approximation is commonly deployed in modern applications with large state space to approximate either the value function or the policy, while self-play enables the learner to improve by playing against itself instead of human experts. While recent progresses in RL theory address a rich set of RL problems, such successes are mostly restricted to the single-agent setting with limited classes of function approximation. This talk presents our progress on addressing both challenges. We first propose a new algorithm that can provably find a near-optimal policy using a polynomial number of samples, for any single-agent RL problems with low Bellman-Eluder dimension---a generic complexity measure which captures a majority of existing tractable RL problems. We then extend the results to the setting of two-player zero-sum Markov games, propose a new self-play algorithm that can provably find the Nash equilibrium policy using a polynomial number of samples. A key component of our self-play algorithm is the exploiter, which facilitates the learning of the main player by deliberately exploiting her weakness.

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