Finding Memoryless Probabilistic Relational Policies for Inter-task Reuse

Relational representations let sequential decision problems be described through objects and relations, resulting in more compact, expressive, and domain-independent representations that make it possible to find and generalize solutions much faster than propositional representations. In this paper we propose a modified policy iteration algorithm (AbsProb-PI) for the infinite-horizon discounted-reward criterion; the algorithm finds a memoryless probabilistic relational abstract policy that abstracts well the solution from source problems so that it can be applied in new, similar problems. Experiments in robotic navigation validate our proposals and show that we can find effective and efficient abstract policies, outperforming solutions by inductive approaches in the literature.