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On Planning and Exploration in Non-discrete Environments

Sebastian B. ThrunGesellschaft für Mathematik und Datenverarbeitung

出版	Gesellschaft für Mathematik und Datenverarbeitung mbH, 1991
URL	http://books.google.com.hk/books?id=TNIlHAAACAAJ&hl=&source=gbs_api

註釋Abstract: "The application of reinforcement learning to control problems has received considerable attention in the last few years [And86, Bar89, Sut84]. In general there are two principles to solve reinforcement learning problems: direct and indirect techniques, both having their advantages and disadvantages. We present a system that combines both methods [TML91, TML90]. By interaction with an unknown environment a world model is progressively constructed using the backpropagation algorithm. For optimizing actions with respect to future reinforcement planning is applied in two steps: An experience network proposes a plan which is subsequently optimized by gradient descent with a chain of model networks.