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.

翻译：本文研究了在强化学习场景中提升多任务训练效率并利用该过程实现迁移学习的潜力。我们识别了实现该目标面临的若干挑战，并提出一种基于参数组合的迁移方法。首先探究了作为迁移基础的多任务强化学习训练优化策略，随后在多种操作任务上开展了一系列迁移实验。结果表明，所提方法不仅能提升多任务训练阶段的性能，还能在样本效率与任务表现两方面实现有效迁移。