State-Constrained Offline Reinforcement Learning

TL;DR

This paper introduces state-constrained offline RL, enabling policies to leverage out-of-distribution actions that lead to in-distribution states, thereby expanding learning capabilities and improving performance on benchmark datasets.

Contribution

The paper proposes a novel state-constrained offline RL framework and introduces StaCQ, a deep learning algorithm that achieves state-of-the-art results and aligns with new theoretical insights.

Findings

StaCQ achieves state-of-the-art performance on D4RL benchmarks.

Theoretical analysis supports the effectiveness of state-constrained offline RL.

The framework allows combining trajectories more effectively.

Abstract

Traditional offline reinforcement learning (RL) methods predominantly operate in a batch-constrained setting. This confines the algorithms to a specific state-action distribution present in the dataset, reducing the effects of distributional shift but restricting the policy to seen actions. In this paper, we alleviate this limitation by introducing state-constrained offline RL, a novel framework that focuses solely on the dataset's state distribution. This approach allows the policy to take high-quality out-of-distribution actions that lead to in-distribution states, significantly enhancing learning potential. The proposed setting not only broadens the learning horizon but also improves the ability to combine different trajectories from the dataset effectively, a desirable property inherent in offline RL. Our research is underpinned by theoretical findings that pave the way for subsequent advancements in this area. Additionally, we introduce StaCQ, a deep learning algorithm that achieves state-of-the-art performance on the D4RL benchmark datasets and aligns with our theoretical propositions. StaCQ establishes a strong baseline for forthcoming explorations in this domain.