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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
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.
@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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