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arXiv · 2201.12175

Safe Policy Improvement Approaches on Discrete Markov Decision Processes

Philipp Scholl·Felix Dietrich·Clemens Otte·Steffen Udluft
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Abstract

Safe Policy Improvement (SPI) aims at provable guarantees that a learned policy is at least approximately as good as a given baseline policy. Building on SPI with Soft Baseline Bootstrapping (Soft-SPIBB) by Nadjahi et al., we identify theoretical issues in their approach, provide a corrected theory, and derive a new algorithm that is provably safe on finite Markov Decision Processes (MDP). Additionally, we provide a heuristic algorithm that exhibits the best performance among many state of the art SPI algorithms on two different benchmarks. Furthermore, we introduce a taxonomy of SPI algorithms and empirically show an interesting property of two classes of SPI algorithms: while the mean performance of algorithms that incorporate the uncertainty as a penalty on the action-value is higher, actively restricting the set of policies more consistently produces good policies and is, thus, safer.

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Philipp Scholl, Felix Dietrich, Clemens Otte, Steffen Udluft. 2022-01-28. Safe Policy Improvement Approaches on Discrete Markov Decision Processes. https://doi.org/10.5220/0010786600003116

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