Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework

• URL: http://arxiv.org/abs/2306.01839v1
• Date: Fri, 2 Jun 2023 18:00:33 GMT
• Title: Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework
• Authors: Lingfeng Sun, Haichao Zhang, Wei Xu, Masayoshi Tomizuka
• Abstract summary: We investigate the potential of improving multi-task training and leveraging it for transferring in the reinforcement learning setting. We propose a transferring approach with a parameter-compositional formulation. Experimental results demonstrate that the proposed approach can have improved performance in the multi-task training stage.
• Score: 44.43196786555784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting. We identify several challenges towards this goal and propose a transferring approach with a parameter-compositional formulation. We investigate ways to improve the training of multi-task reinforcement learning which serves as the foundation for transferring. Then we conduct a number of transferring experiments on various manipulation tasks. Experimental results demonstrate that the proposed approach can have improved performance in the multi-task training stage, and further show effective transferring in terms of both sample efficiency and performance.

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