Symbolic Generalization for On-line Planning

Symbolic representations have been used successfully in off-line planning algorithms for Markov decision processes. We show that they can also improve the performance of on-line planners. In addition to reducing computation time, symbolic generalization can reduce the amount of costly real-world int...

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Bibliographic Details
Published inarXiv.org
Main Authors Feng, Zhengzhu, Hansen, Eric A, Zilberstein, Shlomo
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.10.2012
Subjects
Algorithms
Central processing units
CPUs
Dynamic programming
Markov processes
Performance enhancement
Planning
Real-time programming
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Summary:Symbolic representations have been used successfully in off-line planning algorithms for Markov decision processes. We show that they can also improve the performance of on-line planners. In addition to reducing computation time, symbolic generalization can reduce the amount of costly real-world interactions required for convergence. We introduce Symbolic Real-Time Dynamic Programming (or sRTDP), an extension of RTDP. After each step of on-line interaction with an environment, sRTDP uses symbolic model-checking techniques to generalizes its experience by updating a group of states rather than a single state. We examine two heuristic approaches to dynamic grouping of states and show that they accelerate the planning process significantly in terms of both CPU time and the number of steps of interaction with the environment.
ISSN:2331-8422