Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, Japan.
Department of Information and Communications Engineering, Shibaura Institute of Technology, Tokyo 135-8548, Japan.
Sensors (Basel). 2022 Sep 14;22(18):6959. doi: 10.3390/s22186959.
Transfer learning is an effective approach for adapting an autonomous agent to a new target task by transferring knowledge learned from the previously learned source task. The major problem with traditional transfer learning is that it only focuses on optimizing learning performance on the target task. Thus, the performance on the target task may be improved in exchange for the deterioration of the source task's performance, resulting in an agent that is not able to revisit the earlier task. Therefore, transfer learning methods are still far from being comparable with the learning capability of humans, as humans can perform well on both source and new target tasks. In order to address this limitation, a task adaptation method for imitation learning is proposed in this paper. Being inspired by the idea of repetition learning in neuroscience, the proposed adaptation method enables the agent to repeatedly review the learned knowledge of the source task, while learning the new knowledge of the target task. This ensures that the learning performance on the target task is high, while the deterioration of the learning performance on the source task is small. A comprehensive evaluation over several simulated tasks with varying difficulty levels shows that the proposed method can provide high and consistent performance on both source and target tasks, outperforming existing transfer learning methods.
迁移学习是一种有效的方法，可以通过从先前学习的源任务中转移知识来适应新的目标任务。传统迁移学习的主要问题是它只关注于优化目标任务上的学习性能。因此，目标任务的性能可能会提高，而源任务的性能可能会恶化，导致代理无法重新访问早期任务。因此，迁移学习方法仍然远远不能与人类的学习能力相媲美，因为人类可以在源任务和新的目标任务上都表现出色。为了解决这个限制，本文提出了一种模仿学习的任务自适应方法。受神经科学中重复学习思想的启发，所提出的自适应方法使代理能够反复复习源任务的所学知识，同时学习目标任务的新知识。这确保了目标任务的学习性能较高，而源任务的学习性能恶化较小。在几个具有不同难度级别的模拟任务上的综合评估表明，所提出的方法可以在源任务和目标任务上提供高且一致的性能，优于现有的迁移学习方法。
