  The online Markov decision process (MDP) is a generalization of the classical
Markov decision process that incorporates changing reward functions. In this
paper, we propose practical online MDP algorithms with policy iteration and
theoretically establish a sublinear regret bound. A notable advantage of the
proposed algorithm is that it can be easily combined with function
approximation, and thus large and possibly continuous state spaces can be
efficiently handled. Through experiments, we demonstrate the usefulness of the
proposed algorithm.
