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.
| Original language | English |
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| Pages | 89-95 |
| Number of pages | 7 |
| State | Published - 1989 |
| Event | IJCNN International Joint Conference on Neural Networks - Washington, DC, USA Duration: 18 Jun 1989 → 22 Jun 1989 |
Other
| Other | IJCNN International Joint Conference on Neural Networks |
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| City | Washington, DC, USA |
| Period | 18/06/89 → 22/06/89 |