Offline-to-online reinforcement learning with efficient unconstrained fine-tuning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Offline reinforcement learning (RL) learns from fixed datasets but is limited by data quality and coverage.
• Offline-to-online RL aims to combine offline and online learning for better sample efficiency.
• Existing methods face challenges adapting to online learning due to distributional shift and conservative training.

Purpose of the Study:

• To develop an efficient unconstrained fine-tuning framework for offline-to-online reinforcement learning.
• To overcome limitations of existing methods in adapting to online environments and improving pre-trained policies.
• To enhance sample efficiency and mitigate bias in value function estimation.

Main Methods:

• Proposed an efficient unconstrained fine-tuning framework that removes conservative constraints during policy updates.
• Leveraged dynamics representation learning to capture meaningful features and accelerate fine-tuning.
• Employed layer normalization to bound Q-values and prevent catastrophic divergence.
• Increased the update frequency of the value network to improve sample efficiency and reduce estimation bias.

Main Results:

• The proposed framework demonstrated superior performance compared to state-of-the-art offline-to-online RL algorithms.
• Achieved significant improvements across various tasks on the D4RL benchmark.
• Required minimal online interactions to outperform existing methods.

Conclusions:

• The efficient unconstrained fine-tuning framework effectively addresses challenges in offline-to-online reinforcement learning.
• The method enables thorough exploration and improves policy performance with high sample efficiency.
• This approach offers a promising direction for advancing reinforcement learning in real-world applications.