While recent work in reinforcement learning (RL) has led to agents capable of solving increasingly complex tasks, the issue of high sample complexity is still a major concern. This issue has motivated the development of additional techniques that augment RL methods in an attempt to increase task learning speed. In particular, inter-agent teaching—endowing agents with the ability to respond to instructions from others—has been responsible for many of these developments. RL agents that can leverage instruction from a more competent teacher have been shown to be able to learn tasks significantly faster than agents that cannot take advantage of such instruction. That said, the inter-agent teaching paradigm presents many new challenges due to, among other factors, differences between the agents involved in the teaching interaction. As a result, many inter-agent teaching methods work only in restricted settings and have proven difficult to generalize to new domains or scenarios. In this article, we propose two frameworks that provide a comprehensive view of the challenges associated with inter-agent teaching. We highlight state-of-the-art solutions, open problems, prospective applications, and argue that new research in this area should be developed in the context of the proposed frameworks.

中文翻译：

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代理商教学代理商：代理商间迁移学习调查

尽管最近在强化学习（RL）中的工作已导致代理能够解决日益复杂的任务，但样本高度复杂的问题仍然是一个主要问题。这个问题促使开发其他技术，这些技术可以增强RL方法，以提高任务学习速度。尤其是，座席间的教学（使座席能够回应他人的指令）对许多此类发展负有责任。已经证明，可以利用能力更强的老师的RL代理比不能利用这种指导的代理能够更快地学习任务。就是说，由于除其他因素外，座席间的教学范式提出了许多新的挑战，其中包括参与教学交互的座席之间的差异。结果是，许多代理间教学方法只能在受限的环境下工作，并且已证明很难推广到新的领域或场景。在本文中，我们提出了两个框架，它们提供了与代理间教学相关的挑战的全面视图。我们重点介绍了最新的解决方案，未解决的问题，预期的应用，并认为应该在所提议的框架内开发该领域的新研究。