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Matthew E. Taylor and Sonia Chernova. Integrating Human Demonstration
and Reinforcement Learning: Initial Results in Human-Agent Transfer. In Proceedings of the Agents Learning Interactively
from Human Teachers workshop (at AAMAS-10), May 2010.
ALIHT-10
This work introduces Human-Agent Transfer (HAT), a method thatcombines 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 can be transferred into abaseline policy for an agent, and reinforcement learning can be usedto significantly improve policy performance. These results are animportant initial step that suggest that agents can not only quicklylearn to mimic human actions, but that they can also learn to surpassthe abilities of the teacher.
@inproceedings(ALIHT10-Taylor, author="Matthew E.\ Taylor and Sonia Chernova", title="Integrating Human Demonstration and Reinforcement Learning: Initial Results in Human-Agent Transfer", Booktitle="Proceedings of the Agents Learning Interactively from Human Teachers workshop (at AAMAS-10)", month="May", year= "2010", wwwnote={<a href="www.cs.utexas.edu/~bradknox/AAMAS-ALIHT/">ALIHT-10</a>}, abstract={ This work introduces Human-Agent Transfer (HAT), a method 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 can be transferred into a baseline policy for an agent, and reinforcement learning can be used to significantly improve policy performance. These results are an important initial step that suggest that agents can not only quickly learn to mimic human actions, but that they can also learn to surpass the abilities of the teacher.}, )
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