From Static Policies to Adaptive Priors in Offline Reinforcement Learning

2026-09-26

AI Summary

This study addresses the challenge of distribution shift that hinders offline reinforcement learning (RL) policies during online interaction, proposing an adaptive offline RL framework. This approach reconceptualizes the offline RL objective from static deployment to constructing a policy prior oriented toward future experiences. By employing Bayesian methods to preserve epistemic uncertainty about the underlying environment, it endows policies with memory, exploration, and self-correction capabilities to facilitate test-time adaptation and online fine-tuning. Furthermore, this work clearly delineates the fundamental distinctions between adaptive offline RL and conventional offline-to-online paradigms. It validates the critical value of policy adaptability under limited data coverage and environmental changes, while identifying open challenges and future directions for the field.

Abstract

Offline reinforcement learning (RL) has traditionally focused on learning policies for direct deployment under conservative objectives, where uncertainty outside the offline dataset is treated pessimistically to ensure robustness. We argue that this formulation becomes incomplete when an offline-trained policy is subsequently updated through online interaction, as increasingly occurs in modern intelligent systems through test-time adaptation and online fine-tuning. This position paper argues that, in such settings, the objective of offline RL should extend beyond immediate deployment and instead prioritize learning adaptive policy priors: policies that preserve the capacity to improve during subsequent interaction through memory, exploration, and self-correction. We formalize this perspective as adaptive offline reinforcement learning (AORL), distinguish it from offline-to-online RL, and explain why adaptability becomes important under distributional shift, limited dataset coverage, and changing test-time conditions. We further discuss Bayesian offline RL as one principled direction for constructing adaptive policy priors by preserving epistemic uncertainty over plausible environments. Finally, we outline connections, open challenges, and research directions for treating offline RL as preparation for future experience rather than as a static deployment problem.

Problem

• Offline Reinforcement Learning
• Adaptive Policy Priors
• Test-time Adaptation
• Distributional Shift
• Online Fine-tuning

Innovation

• Adaptive Offline Reinforcement Learning
• Adaptive Policy Priors
• Bayesian Offline RL
• Epistemic Uncertainty
• Test-time Adaptation