Thompson Sampling is Asymptotically Optimal in General Environments
  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. 
Jan Leike and Tor Lattimore and Laurent Orseau and Marcus Hutter
cs.LG cs.AI stat.ML