Hundreds Guide Millions: Adaptive Offline Reinforcement Learning With Expert Guidance

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Offline reinforcement learning (RL) trains agents using pre-collected datasets without real-time interaction.
• A key challenge in offline RL is the distributional shift problem, where training and deployment data distributions differ.
• Current methods often apply uniform policy constraints, which may not be optimal for all data points.

Purpose of the Study:

• To propose a novel approach for offline reinforcement learning that addresses the limitations of uniform policy constraints.
• To introduce a method that adaptively adjusts policy constraints based on individual data sample characteristics.
• To enhance the performance and robustness of offline RL algorithms.

Main Methods:

• Introduced Guided Offline Reinforcement Learning (GORL), a plug-in approach.
• Developed a guiding network that utilizes expert demonstrations.
• The guiding network adaptively determines the balance between policy improvement and policy constraint for each sample.

Main Results:

• Theoretically proved the rationality and near-optimality of the GORL guidance mechanism.
• Demonstrated significant performance improvements across various environments through extensive experiments.
• Showcased GORL's compatibility and effectiveness when integrated with existing offline RL algorithms.

Conclusions:

• Adaptive policy constraint is crucial for mitigating distributional shift in offline RL.
• GORL offers a flexible and effective solution for improving offline RL performance.
• The proposed method provides statistically significant benefits and is easily applicable to various offline RL frameworks.