Bayesian Models of Nonstationary Markov Decision Processes
Nicholas K. Jong, Peter Stone · 2005
Standard reinforcement learning algorithms generate polices that optimize expected future rewards in a priori unknown domains, but they assume that the domain does not change over time. Prior work cast the reinforcement learning problem as a Bayesian estimation problem, using experience data to condition a probability distribution over domains. In this paper we propose an elaboration of the typical Bayesian model that accounts for the possibility that some aspect of the domain changes spontaneously during learning. We develop a reinforcement learning algorithm based on this model that we expect to react more intelligently to sudden changes in the behavior of the environment.