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Optimal planning with approximate model-based reinforcement learning

University of British Columbia

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.

Text

2012-01-04

Attribution-ShareAlike 3.0 Unported

http://hdl.handle.net/2429/39889

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-sa/3.0/