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The Utility of Temporal Abstraction in Reinforcement Learning.
Nicholas
K. Jong, Todd Hester, and Peter
Stone.
In The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, May 2008.
AAMAS-2008
[PDF]136.4kB [postscript]325.7kB
The hierarchical structure of real-world problems has motivated extensive research into temporal abstractions for reinforcement learning, but precisely how these abstractions allow agents to improve their learning performance is not well understood. This paper investigates the connection between temporal abstraction and an agent's exploration policy, which determines how the agent's performance improves over time. Experimental results with standard methods for incorporating temporal abstractions show that these methods benefit learning only in limited contexts. The primary contribution of this paper is a clearer understanding of how hierarchical decompositions interact with reinforcement learning algorithms, with important consequences for the manual design or automatic discovery of action hierarchies.
@InProceedings{AAMAS08-jong,
author="Nicholas K.\ Jong and Todd Hester and Peter Stone",
title="The Utility of Temporal Abstraction in Reinforcement Learning",
booktitle="The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems",
month="May",
year="2008",
abstract={ The hierarchical structure of real-world problems has
motivated extensive research into temporal
abstractions for reinforcement learning, but
precisely how these abstractions allow agents to
improve their learning performance is not well
understood. This paper investigates the connection
between temporal abstraction and an agent's
exploration policy, which determines how the agent's
performance improves over time. Experimental results
with standard methods for incorporating temporal
abstractions show that these methods benefit learning
only in limited contexts. The primary contribution
of this paper is a clearer understanding of how
hierarchical decompositions interact with
reinforcement learning algorithms, with important
consequences for the manual design or automatic
discovery of action hierarchies. },
wwwnote={<a href="http://gaips.inesc-id.pt/aamas2008/">AAMAS-2008</a>},
}
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