Title :
Reinforcement learning algorithms as function optimizers
Author :
Williams, Ronald J. ; Peng, Jing
Author_Institution :
Coll. of Comput. Sci. Northeastern Univ., Boston, MA, USA
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.<>
Keywords :
adaptive systems; learning systems; neural nets; optimisation; REINFORCE algorithms; adaptive trial generator; deterministic functions; function optimization; reinforcement learning algorithm; simulations; Adaptive systems; Learning systems; Neural networks; Optimization methods;
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
DOI :
10.1109/IJCNN.1989.118683
