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arXiv · 2306.01839

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

Lingfeng Sun·Haichao Zhang·Wei Xu·Masayoshi Tomizuka
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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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Lingfeng Sun, Haichao Zhang, Wei Xu, Masayoshi Tomizuka. 2023-06-02. Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework. https://arxiv.org/abs/2306.01839

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