Model-Based Online Learning of POMDPs

Abstract. Learning to act in an unknown partially observable domain is a difficult variant of the reinforcement learning paradigm. Research in the area has focused on model-free methods — methods that learn a policy without learning a model of the world. When sensor noise increases, model-free methods provide less accurate policies. The model-based approach — learning a POMDP model of the world, and computing an optimal policy for the learned model — may generate superior results in the presence of sensor noise, but learning and solving a model of the environment is a difficult problem. We have previously shown how such a model can be obtained from the learned policy of model-free methods, but this approach implies a distinction between a learning phase and an acting phase that is undesirable. In this paper we present a novel method for learning a POMDP model online, based on McCallums’ Utile Suffix Memory (USM), in conjunction with an approximate policy obtained using an in...

• Type: Conference
• Year: 2005
• Where: ECML
• Authors: Guy Shani, Ronen I. Brafman, Solomon Eyal Shimony
• Keywords: ECML 2005, Model-free Methods, Policy, Sensor Noise