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19 April 2024

» arxiv » 1602.7905

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Thompson Sampling is Asymptotically Optimal in General Environments
Jan Leike ; Tor Lattimore ; Laurent Orseau ; Marcus Hutter ;
Date:	25 Feb 2016
Abstract:	We discuss a variant of Thompson sampling for nonparametric reinforcement learning in a countable classes of general stochastic environments. These environments can be non-Markov, non-ergodic, and partially observable. We show that Thompson sampling learns the environment class in the sense that (1) asymptotically its value converges to the optimal value in mean and (2) given a recoverability assumption regret is sublinear.
Source:	arXiv, 1602.7905
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