Reinforcement learning algorithms as function optimizers

Ronald J. Williams, Jing Peng

Research output: Contribution to conferencePaperpeer-review

18 Scopus citations


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.

Original languageEnglish
Number of pages7
StatePublished - 1989
EventIJCNN International Joint Conference on Neural Networks - Washington, DC, USA
Duration: 18 Jun 198922 Jun 1989


OtherIJCNN International Joint Conference on Neural Networks
CityWashington, DC, USA


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