  The partially observable Markov decision process (POMDP) provides a
principled general model for planning under uncertainty. However, solving a
general POMDP is computationally intractable in the worst case. This paper
introduces POMDP-lite, a subclass of POMDPs in which the hidden state variables
are constant or only change deterministically. We show that a POMDP-lite is
equivalent to a set of fully observable Markov decision processes indexed by a
hidden parameter and is useful for modeling a variety of interesting robotic
tasks. We develop a simple model-based Bayesian reinforcement learning
algorithm to solve POMDP-lite models. The algorithm performs well on
large-scale POMDP-lite models with up to $10^{20}$ states and outperforms the
state-of-the-art general-purpose POMDP algorithms. We further show that the
algorithm is near-Bayesian-optimal under suitable conditions.
