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.
|Number of pages||7|
|State||Published - 1 Dec 1989|
|Event||IJCNN International Joint Conference on Neural Networks - Washington, DC, USA|
Duration: 18 Jun 1989 → 22 Jun 1989
|Other||IJCNN International Joint Conference on Neural Networks|
|City||Washington, DC, USA|
|Period||18/06/89 → 22/06/89|