Planning in entropy-regularized Markov decision processes and games

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 Oe(1/ε4 ) for a desired accuracy ε, whereas for non-regularized settings there are no known algorithms with guaranteed polynomial sample complexity in the worst case.

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