Peter Stone's Selected Publications

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Agents teaching agents: a survey on inter-agent transfer learning

Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone. Agents teaching agents: a survey on inter-agent transfer learning. Autonomous Agents and Multi-Agent Systems, Dec 2019.
Official version from JAAMAS

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Abstract

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.

BibTeX Entry

@article{JAAMAS20-Leno,
  author = {Felipe Leno Da Silva and 
               Garrett Warnell and Anna Helena Reali Costa and Peter Stone},
  title = {Agents teaching agents: a survey on inter-agent transfer learning},
  journal = {Autonomous Agents and Multi-Agent Systems},
  year = {2019},
  abstract = {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.},
  wwwnote={Official version from <a href="https://link.springer.com/article/10.1007/s10458-019-09430-0">JAAMAS</a>},
  location = {Germany},
  month = {Dec}
}

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