From Static to Dynamic: Leveraging Implicit Behavioral Models to Facilitate Transition in Offline-to-Online Reinforcement Learning

Lipeng Zu, Xiaonan Zhang, Yukun Yuan, Shayok Chakraborty

Keywords: Offline-to-Online Reinforcement Learning, Behavioral Adaptation, Q-value Estimation, Priority Sampling Strategy

Abstract: Transitioning reinforcement learning (RL) models from offline training environments to dynamic online settings faces critical challenges because of the distributional shift and the model inability in effectively adapting to new, unseen scenarios. This work proposes the \textbf{B}ehavior \textbf{A}daption \textbf{Q}-Learning (BAQ), a novel framework facilitating smoother transitions in offline-to-online RL. BAQ strategically leverages the implicit behavioral model to imitate and adapt behaviors of offline datasets, enabling the model to handle out-of-distribution state-action pairs more effectively during its online deployment. The key to our approach is the integration of a composite loss function that not only mimics the offline data-driven policy but also dynamically adjusts to new experiences encountered online. This dual-focus mechanism enhances the model's adaptability and robustness, reducing Q-value estimation errors and improving the overall learning efficiency. Extensive empirical evaluations demonstrate that BAQ significantly outperforms existing methods, achieving enhanced adaptability and reduced performance degradation in diverse RL settings. Our framework sets a new standard for offline-to-online RL, offering a robust solution for applications requiring reliable transitions from theoretical training to practical, real-world execution.

Primary Area: reinforcement learning

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Submission Number: 13163