Planning in entropy-regularized Markov decision processes and games

Code

• github.com/omardrwch/smoothcruiser-check
  Officialnone★ 0

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 SmoothCruiser. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order O(1/^4) for a desired accuracy , whereas for non-regularized settings there are no known algorithms with guaranteed polynomial sample complexity in the worst case.

Reproductions