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

ICLR 2025 Conference Submission13163 Authors

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

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 13163