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arXiv · 2604.19695

Planning in entropy-regularized Markov decision processes and games

Jean-Bastien Grill·Omar Darwiche Domingues·Pierre Ménard·Rémi Munos·Michal Valko
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Abstract

We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the environment. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order O~(1/epsilon^4) for a desired accuracy epsilon, whereas for non-regularized settings there are no known algorithms with guaranteed polynomial sample complexity in the worst case.

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Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos, Michal Valko. 2026-04-21. Planning in entropy-regularized Markov decision processes and games. https://arxiv.org/abs/2604.19695

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