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In this paper we consider infinite horizon discounted dynamic programmingproblems with finite state and control spaces, and partial state observations.We discuss an algorithm that uses multistep lookahead, truncated rollout with aknown base policy, and a terminal cost function approximation. This algorithmis also used for policy improvement in an approximate policy iteration scheme,where successive policies are approximated by using a neural networkclassifier. A novel feature of our approach is that it is well suited fordistributed computation through an extended belief space formulation and theuse of a partitioned architecture, which is trained with multiple neuralnetworks. We apply our methods in simulation to a class of sequential repairproblems where a robot inspects and repairs a pipeline with potentially severalrupture sites under partial information about the state of the pipeline.

Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration with Application to Autonomous Sequential Repair Problems