There is an implicit assumption in machine learning techniques that each new task has no relation to the tasks previously learned. Therefore, tasks are often addressed independently. However, in some domains, particularly reinforcement learning (RL), this assumption is often incorrect because tasks in the same or similar domain tend to be related. In other words, even though tasks are quite different in their specifics, they may have general similarities, such as shared skills, making them related. In this paper, a novel domain adaptation-based method using adversarial networks is proposed to do transfer learning in RL problems. Our proposed method incorporates skills previously learned from source task to speed up learning on a new target task by providing generalization not only within a task but also across different, but related tasks. The experimental results indicate the effectiveness of our method in dealing with RL problems.

中文翻译：

---

使用对抗网络的基于域适应的迁移学习

机器学习技术中有一个隐含的假设，即每个新任务与之前学习的任务无关。因此，任务通常是独立处理的。然而，在某些领域，特别是强化学习 (RL)，这种假设通常是不正确的，因为相同或相似领域中的任务往往是相关的。换句话说，即使任务在细节上大相径庭，但它们可能具有普遍的相似性，例如共享技能，从而使它们相关联。在本文中，提出了一种使用对抗网络的基于域适应的新方法来在 RL 问题中进行迁移学习。我们提出的方法结合了以前从源任务中学到的技能，通过不仅在任务内而且在不同但相关的任务之间提供泛化来加速新目标任务的学习。