Actor-Critic Alignment for Offline-to-Online Reinforcement Learning.

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Deep offline reinforcement learning (RL) leverages datasets to create high-quality models, reducing online fine-tuning needs.
• State-action distribution shift in offline RL can lead to significant errors, degrading initial policy performance.
• Current solutions often require complex online estimations of distribution divergence or density ratios.

Purpose of the Study:

• To propose a novel approach for deep offline reinforcement learning that mitigates issues caused by distribution shift.
• To develop a method that avoids complex online estimations by post-processing value functions.
• To enable simpler online fine-tuning procedures within actor-critic frameworks.

Main Methods:

• Deviating from standard actor-critic methods that directly transfer state-action value functions.
• Post-processing learned value functions to align with the offline policy.
• Taming Q-values for actions outside the learned offline policy distribution.

Main Results:

• The proposed method successfully improves the performance of fine-tuned robotic agents.
• Empirical results demonstrate enhanced performance across various simulated robotic tasks.
• The approach simplifies the online fine-tuning process, making it comparable to standard actor-critic algorithms.

Conclusions:

• The novel post-processing technique effectively addresses challenges posed by distribution shift in offline RL.
• This method offers a more straightforward way to achieve high-performing policies through simplified online fine-tuning.
• The approach shows significant promise for practical applications in robotics and other domains relying on offline RL.