Behavior-Adaptive Q-Learning: A Unifying Framework for Offline-to-Online RL

2025-11-05

AI Summary

Offline reinforcement learning (RL) policies often suffer from severe performance degradation when deployed in dynamic environments due to distributional shift and inaccurate value estimation on out-of-distribution (OOD) state-action pairs. To address this, we propose a smooth transition framework enabling reliable offline-to-online adaptation. Our method introduces three key components: (1) an implicit behavioral model that generates behavior-consistency signals to regularize policy updates; (2) an uncertainty-aware dual-objective loss function that jointly optimizes for both policy conservatism and environmental adaptability; and (3) an online confidence-driven constraint relaxation mechanism that dynamically balances exploration and stability. Evaluated across multiple benchmark tasks, our approach significantly improves recovery speed, robustness to environmental changes, and final asymptotic performance. Empirical results demonstrate its effectiveness in enabling safe, stable, and efficient transfer from offline pretraining to online deployment in realistic, non-stationary settings.

Technology Category

• Machine Learning: Online Learning & Bandits
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Behavior Learning & Control

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Offline reinforcement learning (RL) enables training from fixed data without online interaction, but policies learned offline often struggle when deployed in dynamic environments due to distributional shift and unreliable value estimates on unseen state-action pairs. We introduce Behavior-Adaptive Q-Learning (BAQ), a framework designed to enable a smooth and reliable transition from offline to online RL. The key idea is to leverage an implicit behavioral model derived from offline data to provide a behavior-consistency signal during online fine-tuning. BAQ incorporates a dual-objective loss that (i) aligns the online policy toward the offline behavior when uncertainty is high, and (ii) gradually relaxes this constraint as more confident online experience is accumulated. This adaptive mechanism reduces error propagation from out-of-distribution estimates, stabilizes early online updates, and accelerates adaptation to new scenarios. Across standard benchmarks, BAQ consistently outperforms prior offline-to-online RL approaches, achieving faster recovery, improved robustness, and higher overall performance. Our results demonstrate that implicit behavior adaptation is a principled and practical solution for reliable real-world policy deployment.

Problem

• Addresses offline RL's struggle with dynamic environments due to distributional shift
• Enables smooth transition from offline training to online fine-tuning using behavioral models
• Reduces error propagation and stabilizes adaptation to new scenarios

Innovation

• Leverages implicit behavioral model from offline data
• Uses dual-objective loss for adaptive policy alignment
• Gradually relaxes constraints as online experience grows