CASA: Bridging the Gap between Policy Improvement and Policy Evaluation with Conflict Averse Policy Iteration

Changnan Xiao, Haosen Shi, Jiajun Fan, Shihong Deng, Haiyan Yin

Keywords: reinforcement learning, policy iteration

TL;DR: This paper proposes a method to eliminate gradient conflicts between policy improvement and policy evaluation.

Abstract: We study the problem of model-free reinforcement learning, which is often solved following the principle of Generalized Policy Iteration (GPI). While GPI is typically an interplay between policy evaluation and policy improvement, most conventional model-free methods with function approximation assume the independence of GPI steps, despite of the inherent connections between them. In this paper, we present a method that attempts to eliminate the inconsistency between policy evaluation step and policy improvement step, leading to a conflict averse GPI solution with gradient-based functional approximation. Our method is capital to balancing exploitation and exploration between policy-based and value-based methods and is applicable to existed policy-based and value-based methods. We conduct extensive experiments to study theoretical properties of our method and demonstrate the effectiveness of our method on Atari 200M benchmark.