Optimal planning with approximate model-based reinforcement learning
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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| Language: | English |
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University of British Columbia
2012
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| Online Access: | http://hdl.handle.net/2429/39889 |