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Title:
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Adaptive Envelope MDPs for Relational Equivalence-based Planning |
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Author:
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Gardiol, Natalia H.; Kaelbling, Leslie Pack |
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Other Contributors:
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Learning and Intelligent Systems |
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Advisor:
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Leslie Kaelbling |
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Issue Date:
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2008-07-29 |
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Abstract:
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We describe a method to use structured representations of the environmentâs dynamics to constrain and speed up the planning process. Given a problem domain described in a probabilistic logical description language, we develop an anytime technique that incrementally improves on an initial, partial policy. This partial solution is found by ï¬rst reducing the number of predicates needed to represent a relaxed version of the problem to a minimum, and then dynamically partitioning the action space into a set of equivalence classes with respect to this minimal representation. Our approach uses the envelope MDP framework, which creates a Markov decision process out of a subset of the full state space as de- termined by the initial partial solution. This strategy permits an agent to begin acting within a restricted part of the full state space and to expand its envelope judiciously as resources permit. |
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URI:
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http://hdl.handle.net/1721.1/41920
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Other Identifiers:
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MIT-CSAIL-TR-2008-050 |
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Related To
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Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
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