January 1, 1989

Reinforcement learning algorithms as function optimizers

Key Points

Key points are not available for this paper at this time.

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.>

Bookmark

Cite This Study

Williams et al. (Sun,) studied this question.

synapsesocial.com/papers/6a1e6e41e6eabd489e68a7cd https://doi.org/https://doi.org/10.1109/ijcnn.1989.118683

Bookmark