Learning to Plan Probabilistically

Search Techniques for Problem Solving Under Uncertainty and Incomplete Information

Volume

Search Techniques for Problem Solving Under Uncertainty and Incomplete Information

Contents

This paper discusses the learning of probabilistic planning without a priori domain-specific knowledge. Different from existing reinforcement learning algorithms that generate only reactive policies and existing probabilistic planning algorithms that requires a substantial amount of a priori knowledge in order to plan, we devise a two-stage bottom-up learning-to-plan process, in which first reinforcement learning/dynamic programming is applied, without the use of a priori domainspecific knowledge, to acquire a reactive policy and then explicit plans are extracted from the learned reactive policy. Plan extraction is based on a beam search algorithm that performs temporal projection in a restricted fashion guided by the value functions resulting from reinforcement learning/dynamic programming.