Optimal planning with approximate model-based reinforcement learning

Optimal planning with approximate model-based reinforcement learning

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Model-based reinforcement learning methods make efficient use of samples by building a model of the environment and planning with it. Compared to model-free methods, they usually take fewer samples to converge to the optimal policy. Despite that efficiency, model-based methods may not learn the op...

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Bibliographic Details
Main Author:	Kao, Hai Feng
Language:	English
Published:	University of British Columbia 2012
Online Access:	http://hdl.handle.net/2429/39889

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