Improving Offline Reinforcement Learning With in-Sample Advantage Regularization for Robot Manipulation.

Area of Science:

• Robotics
• Machine Learning
• Artificial Intelligence

Background:

• Offline reinforcement learning (RL) enables policy learning from fixed datasets, avoiding risky real-time exploration for improved robot efficiency and safety.
• Existing offline RL methods often struggle with out-of-distribution actions, leading to constrained policies and added complexity.
• Adapting offline RL to robotic manipulation requires minimizing changes and avoiding out-of-distribution action evaluation.

Purpose of the Study:

• To adapt offline reinforcement learning for robotic manipulation with minimal modifications.
• To mitigate the impact of unseen actions in offline RL by avoiding out-of-distribution action evaluation.
• To introduce a simple, efficient, and easy-to-implement method for offline RL in robotics.

Main Methods:

• Improve offline RL using In-Sample Advantage Regularization (ISAR).
• ISAR learns the state-value function using only dataset samples to regress the optimal action-value function, mitigating unseen action impact.
• Calculates the advantage function based on in-sample value estimation and incorporates behavior cloning (BC) regularization during policy updates.

Main Results:

• ISAR achieves performance comparable to state-of-the-art algorithms on D4RL robot benchmarks and sparse reward robotic tasks.
• The method demonstrates excellent performance without requiring complex hyperparameter tuning or excessive training time.
• Effectiveness of ISAR is validated on a real-world robot platform.

Conclusions:

• ISAR offers a simple and effective approach to enhance sample efficiency in offline RL for robotic manipulation.
• The method successfully addresses challenges posed by unseen actions without introducing significant complexity.
• ISAR shows strong potential for practical application in real-world robotic systems.