Reinforcement Programming for function approximation

S. Rana, M. Crowe, C. Fyfe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Reinforcement learning is one of the major strands of current computational intelligence: it is used to enable an agent to explore an environment in order to ascertain the best actions in that environment. Genetic programming is a method to evolve programs and given the similarity between genetic algorithms and reinforcement learning, it is perhaps surprising that so little attention has been given to using reinforcement learning to identify useful programs. This paper makes a start on this task by investigating using reinforcement learning methods for function approximation.
Original languageEnglish
Title of host publication12th UK Workshop on Computational Intelligence (UKCI), 2012
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Print)978-1-4673-4391-6
DOIs
Publication statusPublished - 1 Sep 2012

Keywords

  • function approximation
  • genetic algorithms
  • learning (artificial intelligence)
  • computational intelligence
  • genetic programming
  • reinforcement learning
  • reinforcement programming
  • Equations
  • Function approximation
  • Genetic programming
  • Learning
  • Mathematical model
  • Programming profession

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