Deep reinforcement learning has shown an ability to achieve super-human performance in solving complex reinforcement learning (RL) tasks only from raw-pixels. However, it fails to reuse knowledge from previously learnt tasks to solve new, unseen ones. Generalizing and reusing knowledge are the fundamental requirements for creating a truly intelligent agent. This work proposes a general method for one-to-one transfer learning based on generative adversarial network model tailored to RL task.

翻译：深层强化学习显示,在解决复杂的强化学习(RL)任务方面,只有从原始像素才能达到超人性化的成绩,但是,它不能重新利用以前学到的知识知识来解决新的、看不见的任务。普及和重新利用知识是创造真正智能剂的基本要求。这项工作提出了基于适合RL任务的基因对抗性网络模式的一对一转让学习的一般方法。