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