Peter Stone's Selected Publications

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IFSA: Incremental Feature-Set Augmentation for Reinforcement Learning Tasks

Mazda Ahmadi, Matthew E. Taylor, and Peter Stone. IFSA: Incremental Feature-Set Augmentation for Reinforcement Learning Tasks. In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007.
BEST PAPER AWARD NOMINEE.
AAMAS-2007

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Abstract

Reinforcement learning is a popular and successful framework for many agent-related problems because only limited environmental feedback is necessary for learning. While many algorithms exist to learn effective policies in such problems, learning is often used to solve real world problems, which typically have large state spaces, and therefore suffer from the ``curse of dimensionality.'' One effective method for speeding-up reinforcement learning algorithms is to leverage expert knowledge. In this paper, we propose a method for dynamically augmenting the agent's feature set in order to speed up value-function-based reinforcement learning. The domain expert divides the feature set into a series of subsets such that a novel problem concept can be learned from each successive subset. Domain knowledge is also used to order the feature subsets in order of their importance for learning. Our algorithm uses the ordered feature subsets to learn tasks significantly faster than if the entire feature set is used from the start. Incremental Feature-Set Augmentation (IFSA) is fully implemented and tested in three different domains: Gridworld, Blackjack and RoboCup Soccer Keepaway. All experiments show that IFSA can significantly speed up learning and motivates the applicability of this novel RL method.

BibTeX Entry

@InProceedings{AAMAS07-ahmadi,
        author="Mazda Ahmadi and Matthew E.\ Taylor and Peter Stone",
        title="{IFSA}: Incremental Feature-Set Augmentation for Reinforcement Learning Tasks",
        booktitle="The Sixth International Joint Conference on Autonomous Agents and  Multiagent Systems",
        month="May",year="2007", 
        abstract={
	    Reinforcement learning is a popular and successful framework for
	    many agent-related problems because only limited environmental
	    feedback is necessary for learning. While many algorithms exist to
	    learn effective policies in such problems, learning is often 
	    used to solve real world problems, which typically have large state
	    spaces, and therefore suffer from the ``curse of dimensionality.''
	    One effective method for speeding-up reinforcement learning algorithms
	    is to leverage expert knowledge. In this paper, we propose a method
	    for dynamically augmenting the agent's feature set in order to
	    speed up value-function-based reinforcement learning. The domain
	    expert divides the feature set into a series of subsets such that a
	    novel problem concept can be learned from each successive
	    subset. Domain knowledge is also used to order the feature subsets in
	    order of their importance for learning. Our algorithm uses the
	    ordered feature subsets to learn tasks significantly faster than if
	    the entire feature set is used from the start. Incremental
	    Feature-Set Augmentation (IFSA) is fully implemented and tested in
	    three different domains: Gridworld, Blackjack and RoboCup Soccer
	    Keepaway. All experiments show that IFSA can significantly speed up
	    learning and motivates the applicability of this novel RL method.},
	wwwnote={<b>BEST PAPER AWARD NOMINEE</b>.<br><a href="http://www.aamas2007.nl/">AAMAS-2007</a>},
}

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