Function Optimization using Connectionist Reinforcement Learning Algorithms

Ronald J. Williams, Jing Peng

Research output: Contribution to journalArticlepeer-review

214 Scopus citations


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.

Original languageEnglish
Pages (from-to)241-268
Number of pages28
JournalConnection Science
Issue number3
StatePublished - 1 Jan 1991


  • Reinforcement learning
  • combinatorial optimization
  • entropy maximization
  • genetic algorithms


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