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 opti...

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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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spelling ndltd-UBC-oai-circle.library.ubc.ca-2429-398892018-01-05T17:25:33Z Optimal planning with approximate model-based reinforcement learning Kao, Hai Feng 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 optimal policy due to structural modeling assumptions. In this thesis, we show that by combining model- based methods with hierarchically optimal recursive Q-learning (HORDQ) under a hierarchical reinforcement learning framework, the proposed approach learns the optimal policy even when the assumptions of the model are not all satisfied. The effectiveness of our approach is demonstrated with the Bus domain and Infinite Mario – a Java implementation of Nintendo’s Super Mario Brothers. Science, Faculty of Computer Science, Department of Graduate 2012-01-04T20:01:51Z 2012-01-04T20:01:51Z 2011 2012-05 Text Thesis/Dissertation http://hdl.handle.net/2429/39889 eng Attribution-ShareAlike 3.0 Unported http://creativecommons.org/licenses/by-sa/3.0/ University of British Columbia
collection NDLTD
language English
sources NDLTD
description 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 optimal policy due to structural modeling assumptions. In this thesis, we show that by combining model- based methods with hierarchically optimal recursive Q-learning (HORDQ) under a hierarchical reinforcement learning framework, the proposed approach learns the optimal policy even when the assumptions of the model are not all satisfied. The effectiveness of our approach is demonstrated with the Bus domain and Infinite Mario – a Java implementation of Nintendo’s Super Mario Brothers. === Science, Faculty of === Computer Science, Department of === Graduate
author Kao, Hai Feng
spellingShingle Kao, Hai Feng
Optimal planning with approximate model-based reinforcement learning
author_facet Kao, Hai Feng
author_sort Kao, Hai Feng
title Optimal planning with approximate model-based reinforcement learning
title_short Optimal planning with approximate model-based reinforcement learning
title_full Optimal planning with approximate model-based reinforcement learning
title_fullStr Optimal planning with approximate model-based reinforcement learning
title_full_unstemmed Optimal planning with approximate model-based reinforcement learning
title_sort optimal planning with approximate model-based reinforcement learning
publisher University of British Columbia
publishDate 2012
url http://hdl.handle.net/2429/39889
work_keys_str_mv AT kaohaifeng optimalplanningwithapproximatemodelbasedreinforcementlearning
_version_ 1718583178208215040

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