Optimal learning: Computational procedures for Bayes -adaptive Markov decision processes
This dissertation considers a particular aspect of sequential decision making under uncertainty in which, at each stage, a decision-making agent operating in an uncertain world takes an action that elicits a reinforcement signal and causes the state of the world to change. Optimal learning is a patt...
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| Language: | ENG |
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ScholarWorks@UMass Amherst
2002
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| Online Access: | https://scholarworks.umass.edu/dissertations/AAI3039353 |