Function Optimization using Connectionist Reinforcement Learning Algorithms
Document Type
Article
Publication Date
1-1-1991
Abstract
Any non-associative reinforcement learning algorithm can be viewed as a method for performing function optimization through (possibly noise-corrupted) sampling of function values. We describe the results of simulations in which the optima of several deterministic functions studied by Ackley were sought using variants of REINFORCE algorithms. Some of the algorithms used here incorporated additional heuristic features resembling certain aspects of some of the algorithms used in Ackley's studies. Differing levels of performance were achieved by the various algorithms investigated, but a number of them performed at a level comparable to the best found in Ackley's studies on a number of the tasks, in spite of their simplicity. One of these variants, called REINFORCEjMENT', represents a novel but principled approach to reinforcement learning in nontrivial networks which incorporates an entropy maximization strategy. This was found to perform especially well on more hierarchically organized tasks.
DOI
10.1080/09540099108946587
Montclair State University Digital Commons Citation
Williams, Ronald J. and Peng, Jing, "Function Optimization using Connectionist Reinforcement Learning Algorithms" (1991). Department of Computer Science Faculty Scholarship and Creative Works. 297.
https://digitalcommons.montclair.edu/compusci-facpubs/297