  Bayesian Reinforcement Learning (RL) is capable of not only incorporating
domain knowledge, but also solving the exploration-exploitation dilemma in a
natural way. As Bayesian RL is intractable except for special cases, previous
work has proposed several approximation methods. However, these methods are
usually too sensitive to parameter values, and finding an acceptable parameter
setting is practically impossible in many applications. In this paper, we
propose a new algorithm that greedily approximates Bayesian RL to achieve
robustness in parameter space. We show that for a desired learning behavior,
our proposed algorithm has a polynomial sample complexity that is lower than
those of existing algorithms. We also demonstrate that the proposed algorithm
naturally outperforms other existing algorithms when the prior distributions
are not significantly misleading. On the other hand, the proposed algorithm
cannot handle greatly misspecified priors as well as the other algorithms can.
This is a natural consequence of the fact that the proposed algorithm is
greedier than the other algorithms. Accordingly, we discuss a way to select an
appropriate algorithm for different tasks based on the algorithms' greediness.
We also introduce a new way of simplifying Bayesian planning, based on which
future work would be able to derive new algorithms.
