Titlebook: Reinforcement Learning; Richard S. Sutton Book 1992 Springer Science+Business Media New York 1992 agents.algorithms.artificial intelligenc

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Technical Note,od for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states..This paper presents and proves in detail a convergence theorem for Q-learning based on that outlined in Watkins (1989)

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Transfer of Learning by Composing Solutions of Elemental Sequential Tasks,s of reinforcement learning have focused on single tasks. In this paper I consider a class of sequential decision tasks (SDTs), called composite sequential decision tasks, formed by temporally concatenating a number of elemental sequential decision tasks. Elemental SIYI’s cannot be decomposed into s

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,The Convergence of TD(λ) for General λ,it still converges, but to a different answer from the least mean squares algorithm. Finally it adapts Watkins’ theorem that Q-learning, his closely related prediction and action learning method, converges with probability one, to demonstrate this strong form of convergence for a slightly modified version of TD.

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A Reinforcement Connectionist Approach to Robot Path Finding in Non-Maze-Like Environments,uts and outputs, (iii) exhibits good noise-tolerance and generalization capabilities, (iv) copes with dynamic environments, and (v) solves an instance of the path finding problem with strong performance demands.

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0893-3405 ychology for almost a century, and that workhas had a very strong impact on the AI/engineering work. One could infact consider all of reinforcement learning to 978-1-4613-6608-9978-1-4615-3618-5Series ISSN 0893-3405