Proceedings Article10.1109/IJCNN.1989.118683
Reinforcement learning algorithms as function optimizers
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TL;DR: The results of simulations in which the optima of several deterministic functions studied by D.H. Ackley were sought using variants of REINFORCE algorithms compare favorably to the best results found by Ackley.
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Abstract: Any nonassociative reinforcement learning algorithm can be viewed as a method for performing function optimization through (possibly noise-corrupted) sampling of function values. A description is given of the results of simulations in which the optima of several deterministic functions studied by D.H. Ackley (Ph.D. Diss., Carnegie-Mellon Univ., 1987) were sought using variants of REINFORCE algorithms. Results obtained for certain of these algorithms compare favorably to the best results found by Ackley. >
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