Iteratively Refined Behavior Regularization for Offline Reinforcement Learning

TL;DR

This paper introduces an iterative refinement approach for behavior regularization in offline reinforcement learning, improving policy robustness and performance by gradually updating the reference policy to avoid out-of-sample actions.

Contribution

It proposes a novel iterative refinement algorithm based on conservative policy iteration that enhances behavior regularization in offline RL, with theoretical guarantees and practical improvements.

Findings

Outperforms state-of-the-art methods on D4RL benchmarks

Capable of learning the in-sample optimal policy in tabular settings

Easy to implement with minimal code modifications

Abstract

One of the fundamental challenges for offline reinforcement learning (RL) is ensuring robustness to data distribution. Whether the data originates from a near-optimal policy or not, we anticipate that an algorithm should demonstrate its ability to learn an effective control policy that seamlessly aligns with the inherent distribution of offline data. Unfortunately, behavior regularization, a simple yet effective offline RL algorithm, tends to struggle in this regard. In this paper, we propose a new algorithm that substantially enhances behavior-regularization based on conservative policy iteration. Our key observation is that by iteratively refining the reference policy used for behavior regularization, conservative policy update guarantees gradually improvement, while also implicitly avoiding querying out-of-sample actions to prevent catastrophic learning failures. We prove that in the tabular setting this algorithm is capable of learning the optimal policy covered by the offline dataset, commonly referred to as the in-sample optimal policy. We then explore several implementation details of the algorithm when function approximations are applied. The resulting algorithm is easy to implement, requiring only a few lines of code modification to existing methods. Experimental results on the D4RL benchmark indicate that our method outperforms previous state-of-the-art baselines in most tasks, clearly demonstrate its superiority over behavior regularization.