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arXiv · 2308.14642

Rate-Optimal Policy Optimization for Linear Markov Decision Processes

Uri Sherman·Alon Cohen·Tomer Koren·Yishay Mansour
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Abstract

We study regret minimization in online episodic linear Markov Decision Processes, and obtain rate-optimal $\widetilde O (\sqrt K)$ regret where $K$ denotes the number of episodes. Our work is the first to establish the optimal (w.r.t.~$K$) rate of convergence in the stochastic setting with bandit feedback using a policy optimization based approach, and the first to establish the optimal (w.r.t.~$K$) rate in the adversarial setup with full information feedback, for which no algorithm with an optimal rate guarantee is currently known.

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Uri Sherman, Alon Cohen, Tomer Koren, Yishay Mansour. 2023-08-28. Rate-Optimal Policy Optimization for Linear Markov Decision Processes. https://arxiv.org/abs/2308.14642

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