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Matthew E. Taylor, Halit Bener Suay, and Sonia Chernova. Using Human
Demonstrations to Improve Reinforcement Learning. In The AAAI 2011 Spring Symposium --- Help Me Help You: Bridging
the Gaps in Human-Agent Collaboration, March 2011.
HMHY2011
This work introduces Human-Agent Transfer (HAT), an algorithmthat combines transfer learning, learning from demonstration andreinforcement learning to achieve rapid learning and high performancein complex domains. Using experiments in a simulated robot soccerdomain, we show that human demonstrations transferred into abaseline policy for an agent and refined using reinforcement learningsignificantly improve both learning time and policy performance.Our evaluation compares three algorithmic approaches to incorporating demonstration rule summaries into transfer learning, and studiesthe impact of demonstration quality and quantity.Our results show that all three transfer methods lead to statistically significant improvement in performance over learning without demonstration.
@inproceedings(AAAI11Symp-Taylor,
author="Matthew E.\ Taylor and Halit Bener Suay and Sonia Chernova",
title="Using Human Demonstrations to Improve Reinforcement Learning",
Booktitle="The {AAAI} 2011 Spring Symposium --- Help Me Help You: Bridging the Gaps in Human-Agent Collaboration",
month="March",
year= "2011",
wwwnote={<a href="www.isi.edu/~maheswar/hmhy2011.html">HMHY2011</a>},
abstract={This work introduces Human-Agent Transfer (HAT), an algorithm
that combines transfer learning, learning from demonstration and
reinforcement learning to achieve rapid learning and high performance
in complex domains. Using experiments in a simulated robot soccer
domain, we show that human demonstrations transferred into a
baseline policy for an agent and refined using reinforcement learning
significantly improve both learning time and policy performance.
Our evaluation compares three algorithmic approaches to incorporating
demonstration rule summaries into transfer learning, and studies
the impact of demonstration quality and quantity.
Our results show that all three transfer methods lead to statistically significant improvement in performance over learning without demonstration. },
)
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