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We study parameterized MDPs (PMDPs) in which the key parameters of interestare unknown and must be learned using Bayesian inference. One key definingfeature of such models is the presence of "uninformative" actions that provideno information about the unknown parameters. We contribute a set of assumptionsfor PMDPs under which Thompson sampling guarantees an asymptotically optimalexpected regret bound of $O(T^{-1})$, which are easily verified for manyclasses of problems such as queuing, inventory control, and dynamic pricing.

Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions