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• W3131233483 abstract "We study the reinforcement learning for finite-horizon episodic Markov decision processes with adversarial reward and full information feedback, where the unknown transition probability function is a linear function of a given feature mapping. We propose an optimistic policy optimization algorithm with Bernstein bonus and show that it can achieve $tilde{O}(dHsqrt{T})$ regret, where $H$ is the length of the episode, $T$ is the number of interaction with the MDP and $d$ is the dimension of the feature mapping. Furthermore, we also prove a matching lower bound of $tilde{Omega}(dHsqrt{T})$ up to logarithmic factors. To the best of our knowledge, this is the first computationally efficient, nearly minimax optimal algorithm for adversarial Markov decision processes with linear function approximation." @default.
• W3131233483 created "2021-03-01" @default.
• W3131233483 creator A5051448391 @default.
• W3131233483 creator A5057534674 @default.
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• W3131233483 date "2021-02-17" @default.
• W3131233483 modified "2023-09-27" @default.
• W3131233483 title "Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation." @default.
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• W3131233483 hasPublicationYear "2021" @default.
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