Empirical Policy Evaluation with Supergraphs

Daniel Vial; Vijay Subramanian

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arXiv · 2002.07905

Empirical Policy Evaluation with Supergraphs

Daniel Vial·Vijay Subramanian

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Abstract

We devise and analyze algorithms for the empirical policy evaluation problem in reinforcement learning. Our algorithms explore backward from high-cost states to find high-value ones, in contrast to forward approaches that work forward from all states. While several papers have demonstrated the utility of backward exploration empirically, we conduct rigorous analyses which show that our algorithms can reduce average-case sample complexity from $O(S \log S)$ to as low as $O(\log S)$.

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Daniel Vial, Vijay Subramanian. 2020-02-18. Empirical Policy Evaluation with Supergraphs. https://arxiv.org/abs/2002.07905

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