Optimization-based approximate dynamic programming
Reinforcement learning algorithms hold promise in many complex domains, such as resource management and planning under uncertainty. Most reinforcement learning algorithms are iterative—they successively approximate the solution based on a set of samples and features. Although these iterative algorit...
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| Language: | ENG |
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ScholarWorks@UMass Amherst
2010
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| Online Access: | https://scholarworks.umass.edu/dissertations/AAI3427564 |