Residuals-based Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2604.01378v1
• Date: Wed, 01 Apr 2026 20:38:13 GMT
• Title: Residuals-based Offline Reinforcement Learning
• Abstract summary: offline reinforcement learning (RL) has received increasing attention for learning policies from previously collected data without interaction with the real environment.<n>We propose a residuals-based offline RL framework for general state and action spaces.
• Score: 16.107192067504418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Offline reinforcement learning (RL) has received increasing attention for learning policies from previously collected data without interaction with the real environment, which is particularly important in high-stakes applications. While a growing body of work has developed offline RL algorithms, these methods often rely on restrictive assumptions about data coverage and suffer from distribution shift. In this paper, we propose a residuals-based offline RL framework for general state and action spaces. Specifically, we define a residuals-based Bellman optimality operator that explicitly incorporates estimation error in learning transition dynamics into policy optimization by leveraging empirical residuals. We show that this Bellman operator is a contraction mapping and identify conditions under which its fixed point is asymptotically optimal and possesses finite-sample guarantees. We further develop a residuals-based offline deep Q-learning (DQN) algorithm. Using a stochastic CartPole environment, we demonstrate the effectiveness of our residuals-based offline DQN algorithm.

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