Papers › Planning in entropy-regularized Markov decision processes and games
Planning in entropy-regularized Markov decision processes and games
Jean-bastien Grill, Omar Darwiche Domingues, Pierre Menard, Remi Munos, Michal Valko
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 SmoothCruiser. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order 𝒪̃(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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