Adaptive Planning for Markov Decision Processes with Uncertain Transition Models via Incremental Feature Dependency Discovery

Adaptive Planning for Markov Decision Processes with Uncertain Transition Models via Incremental Feature Dependency Discovery

Solving large scale sequential decision making problems without prior knowledge of the state transition model is a key problem in the planning literature. One approach to tackle this problem is to learn the state transition model online using limited observed measurements. We present an adaptive fun...

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Bibliographic Details
Main Authors: Geramifard, Alborz (Contributor), Chowdhary, Girish (Contributor), How, Jonathan P. (Contributor), Ure, Nazim Kemal (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor)
Format: Article
Language:English
Published: Springer-Verlag, 2013-10-25T13:18:47Z.
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