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arXiv · 1602.07905

Thompson Sampling is Asymptotically Optimal in General Environments

Jan Leike·Tor Lattimore·Laurent Orseau·Marcus Hutter
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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.

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Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter. 2016-02-25. Thompson Sampling is Asymptotically Optimal in General Environments. https://arxiv.org/abs/1602.07905

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