Matthew E. Taylor's Publications

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Teaching on a Budget: Agents Advising Agents in Reinforcement Learning

Lisa Torrey and Matthew E. Taylor. Teaching on a Budget: Agents Advising Agents in Reinforcement Learning. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2013. 23% acceptance rate
AAMAS-13

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Abstract

This paper introduces a teacher-student framework for reinforcementlearning. In this framework, a teacher agent instructs a studentagent by suggesting actions the student should take as it learns.However, the teacher may only give such advice a limited numberof times. We present several novel algorithms that teachers canuse to budget their advice effectively, and we evaluate them in twoexperimental domains: Mountain Car and Pac-Man. Our resultsshow that the same amount of advice, given at different moments,can have different effects on student learning, and that teachers cansignificantly affect student learning even when students use differentlearning methods and state representations.

BibTeX Entry

@inproceedings{AAMAS13-Torrey,
 author="Lisa Torrey and Matthew E.\ Taylor",
 title="Teaching on a Budget: Agents Advising Agents in Reinforcement Learning",
 booktitle = {International Conference on Autonomous Agents and Multiagent Systems ({AAMAS})},
 month="May",
 year = {2013},
 note = {23% acceptance rate},
wwwnote = {<a href="aamas2013.cs.umn.edu/">AAMAS-13</a>},
  abstract = "This paper introduces a teacher-student framework for reinforcement
learning. In this framework, a teacher agent instructs a student
agent by suggesting actions the student should take as it learns.
However, the teacher may only give such advice a limited number
of times. We present several novel algorithms that teachers can
use to budget their advice effectively, and we evaluate them in two
experimental domains: Mountain Car and Pac-Man. Our results
show that the same amount of advice, given at different moments,
can have different effects on student learning, and that teachers can
significantly affect student learning even when students use different
learning methods and state representations.",
}

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