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- W120804890 abstract "Decision theory addresses the task of choosing an action; it provides robust decision-making criteria that support decision-making under conditions of uncertainty or risk. Decision theory has been applied to produce reinforcement learning algorithms that manage uncertainty in state-transitions. However, performance when there is uncertainty regarding the selection of future actions must also be considered, since reinforcement learning tasks are multiple-step decision problems. This work proposes beta-pessimistic Q-learning—a reinforcement learning algorithm that does not assume complete control." @default.
- W120804890 created "2016-06-24" @default.
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- W120804890 date "2003-01-01" @default.
- W120804890 modified "2023-09-26" @default.
- W120804890 title "Reinforcement learning under circumstances beyond its control" @default.
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