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Bayesian Reinforcement Learning (RL) is capable of not only incorporatingdomain knowledge, but also solving the exploration-exploitation dilemma in anatural way. As Bayesian RL is intractable except for special cases, previouswork has proposed several approximation methods. However, these methods areusually too sensitive to parameter values, and finding an acceptable parametersetting is practically impossible in many applications. In this paper, wepropose a new algorithm that greedily approximates Bayesian RL to achieverobustness in parameter space. We show that for a desired learning behavior,our proposed algorithm has a polynomial sample complexity that is lower thanthose of existing algorithms. We also demonstrate that the proposed algorithmnaturally outperforms other existing algorithms when the prior distributionsare not significantly misleading. On the other hand, the proposed algorithmcannot handle greatly misspecified priors as well as the other algorithms can.This is a natural consequence of the fact that the proposed algorithm isgreedier than the other algorithms. Accordingly, we discuss a way to select anappropriate algorithm for different tasks based on the algorithms' greediness.We also introduce a new way of simplifying Bayesian planning, based on whichfuture work would be able to derive new algorithms.

A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model