Off-Policy Policy Gradient with State Distribution Correction

TL;DR

This paper introduces a novel off-policy policy gradient method that corrects for state distribution mismatch, leading to improved policy optimization in Markov decision processes with theoretical guarantees and empirical success.

Contribution

It develops a new off-policy policy gradient technique that accounts for state distribution mismatch, enhancing policy learning accuracy.

Findings

Significant improvement over baselines in policy quality

Theoretical convergence guarantee for the proposed method

Effective correction for state distribution mismatch

Abstract

We study the problem of off-policy policy optimization in Markov decision processes, and develop a novel off-policy policy gradient method. Prior off-policy policy gradient approaches have generally ignored the mismatch between the distribution of states visited under the behavior policy used to collect data, and what would be the distribution of states under the learned policy. Here we build on recent progress for estimating the ratio of the state distributions under behavior and evaluation policies for policy evaluation, and present an off-policy policy gradient optimization technique that can account for this mismatch in distributions. We present an illustrative example of why this is important and a theoretical convergence guarantee for our approach. Empirically, we compare our method in simulations to several strong baselines which do not correct for this mismatch, significantly improving in the quality of the policy discovered.