Agents teaching agents: a survey on inter-agent transfer learning (2020)
Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone
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.
Autonomous Agents and Multi-Agent Systems (2020).

Peter Stone Faculty pstone [at] cs utexas edu
Garrett Warnell Research Scientist warnellg [at] cs utexas edu