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Author: Behboudian, Paniz
Departments: Department of Computing Science
Item type: Thesis
Languages: English
Collections: Graduate and Postdoctoral Studies (GPS), Faculty of
Collections: Graduate and Postdoctoral Studies (GPS), Faculty of /Theses and Dissertations
Supervisors: Bowling, Michael (Computing Science)
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Spring 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...
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