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

Abstract

Offline reinforcement learning (RL) aims to learn the possible policy from a fixed dataset without real-time interactions with the environment. By avoiding the risky exploration of the robot, this approach is expected to significantly improve the robot's learning efficiency and safety. However, due to errors in value estimation from out-of-distribution actions, most offline RL algorithms constrain or regularize the policy to the actions contained within the dataset. The cost of such methods is the introduction of new hyperparameters and additional complexity. In this article, we aim to adapt offline RL to robotic manipulation with minimal changes and to avoid evaluating out-of-distribution actions as much as possible. Therefore, we improve offline RL with in-sample advantage regularization (ISAR). To mitigate the impact of unseen actions, the ISAR learns the state-value function only with the dataset sample to regress the optimal action-value function. Our method calculates the advantage function of action-state pairs based on in-sample value estimation and adds a behavior cloning (BC) regularization term in the policy update. This improves sample efficiency with minimal changes, resulting in a simple and easy-to-implement method. The experiments of the D4RL robot benchmark and multigoal sparse rewards robotic tasks show that the ISAR achieves excellent performance comparable to current state-of-the-art algorithms without the need for complex parameter tuning and too much training time. In addition, we demonstrate the effectiveness of our method on a real-world robot platform.