Improving Value Function Approximation in Factored POMDPs by Exploiting Model Structure

Linear value function approximation in Markov decision processes (MDPs) has been studied extensively, but there are several challenges when applying such techniques to partially observable MDPs (POMDPs). Furthermore, the system designer often has to choose a set of basis functions. We propose an automatic method to derive a suitable set of basis functions by exploiting the structure of factored models. We experimentally show that our approximation can reduce the solution size by several orders of magnitude in large problems.