Reinforcement Programming for function approximation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Salah Rana and Malcolm Crowe and Colin Fyfe",
  booktitle =    "12th UK Workshop on Computational Intelligence (UKCI
  title =        "Reinforcement Programming for function approximation",
  year =         "2012",
  month =        "5-7 " # sep,
  address =      "Edinburgh",
  isbn13 =       "978-1-4673-4391-6",
  DOI =          "doi:10.1109/UKCI.2012.6335777",
  size =         "5 pages",
  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
  keywords =     "genetic algorithms, genetic programming, function
                 approximation, learning (artificial intelligence),
                 computational intelligence, function approximation,
                 reinforcement learning, reinforcement programming,
                 Equations, Function approximation, Learning,
                 Mathematical model, Programming profession",
  notes =        "Also known as \cite{6335777}",

Genetic Programming entries for Salah Aziz Rana Malcolm Crowe Colin Fyfe