Parallel Least-Squares Policy Iteration

Abstract

Inspired by recent progress in parallel and distributed optimization, we propose parallel least-squares policy iteration (parallel LSPI) in this paper. LSPI is a policy iteration method to find an optimal policy for MDPs. As solving MDPs with large state space is challenging and time demanding, we propose a parallel variant of LSPI which is capable of leveraging multiple computational resources. Preliminary analysis of our proposed method shows that the sample complexity improved from O(1/√n) towards O(1/√Mn) for each worker, where n is the number of samples and M is the number of workers. Experiments show the advantages of parallel LSPI comparing to the standard non-parallel one.