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Genetic Reinforcement Learning for Multilayered Neural Networks

L. Darrell Whitley

出版	Colorado State University, Department of Computer Science, 1991
URL	http://books.google.com.hk/books?id=f0PGpwAACAAJ&hl=&source=gbs_api

註釋

Abstract: "Empirical tests indicate that the genetic algorithms which have produced good performance for neural network weight optimization are really genetic hill-climbers, with a strong reliance on mutation rather than hyperplane sampling. Initial results are presented using genetic hill-climbers for reinforcement learning with multilayer neural networks for the control of a simulated cart-centering and pole-balancing dynamical system. 'Genetic reinforcement learning' produces competitive results with AHC, a well-known reinforcement learning paradigm for neural networks that employs temporal difference methods."