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- W2187539077 abstract "a b s t r a c t Humanshavetheabilitytomakeuseofexperiencewhileselectingtheircontrolactionsfordistinctand changing situations, and their process speeds up and have enhanced effectiveness as more experience is gained. In contrast, current technological implementations slow down as more knowledge is stored. A novel way of employing Approximate (or Adaptive) Dynamic Programming (ADP) is described that shifts the underlying Adaptive Critic type of Reinforcement Learning method ‘‘up a level’’, away from designing individual (optimal) controllers to that of developing on-line algorithms that efficiently and" @default.
- W2187539077 created "2016-06-24" @default.
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- W2187539077 date "2009-01-01" @default.
- W2187539077 modified "2023-09-27" @default.
- W2187539077 title "Adaptivedynamicprogrammingapproachtoexperience-basedsystems identificationandcontrol" @default.
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