A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Offline reinforcement learning (RL) enables data-driven agent training but often yields suboptimal performance due to limited dataset quality.
• Fine-tuning agents with online interactions is crucial for deployment, yet faces challenges like constrained exploration and distribution shift.

Purpose of the Study:

• To propose a Simple Unified Uncertainty-guided (SUNG) framework to effectively bridge offline and online reinforcement learning stages.
• To address the key challenges of constrained exploratory behavior and state-action distribution shift in offline-to-online RL.

Main Methods:

• SUNG quantifies uncertainty using a variational autoencoder (VAE)-based state-action visitation density estimator.
• An optimistic exploration strategy selects actions with high value and uncertainty.
• An adaptive exploitation method balances conservative offline RL objectives with standard online RL objectives based on uncertainty.

Main Results:

• SUNG demonstrates state-of-the-art online finetuning performance across diverse environments and datasets within the D4RL benchmark.
• The framework successfully integrates with various existing offline RL methods.
• The proposed uncertainty quantification and guided exploration/exploitation strategies effectively mitigate offline-to-online transfer challenges.

Conclusions:

• The SUNG framework offers a unified and effective solution for enhancing offline RL agents through online fine-tuning.
• Uncertainty estimation is a powerful tool for guiding exploration and managing distribution shift in RL.
• SUNG provides a practical approach to improve agent performance and reliability before real-world deployment.