Unknown mixing times in apprenticeship and reinforcement learning

Tom Zahavy; Alon Cohen; Haim Kaplan; Yishay Mansour

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arXiv · 1905.09704

Unknown mixing times in apprenticeship and reinforcement learning

Tom Zahavy·Alon Cohen·Haim Kaplan·Yishay Mansour

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Abstract

We derive and analyze learning algorithms for apprenticeship learning, policy evaluation, and policy gradient for average reward criteria. Existing algorithms explicitly require an upper bound on the mixing time. In contrast, we build on ideas from Markov chain theory and derive sampling algorithms that do not require such an upper bound. For these algorithms, we provide theoretical bounds on their sample-complexity and running time.

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Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour. 2019-05-23. Unknown mixing times in apprenticeship and reinforcement learning. https://arxiv.org/abs/1905.09704

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