Hierarchical Reinforcement Learning with Grammar-Directed GA-P

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Santiago {Garcia Carbajal} and Nouhad J. Rizk",
  title =        "Hierarchical Reinforcement Learning with
                 Grammar-Directed GA-P",
  journal =      "International Journal of Soft Computing",
  year =         "2006",
  volume =       "1",
  number =       "1",
  pages =        "52--60",
  month =        mar,
  email =        "carbajal@lsi.uniovi.es",
  keywords =     "genetic algorithms, genetic programming, reinforcement
                 learning, grammar, knowledge",
  ISSN =         "1816-9503",
  URL =          "http://medwelljournals.com/abstract/?doi=ijscomp.2006.52.60",
  abstract =     "This article proposes a grammatical approach to
                 hierarchical reinforcement learning.It is based on the
                 grammatical description of a problem,a complex task,or
                 objective.The use of a grammar to control the learning
                 process,constraining the structure of the solutions
                 generated with standard GP, permits the inclusion of
                 knowledge about the problem in a straightforward
                 manner,if this knowledge exists.When the problem to be
                 solved involves the use of fuzzy concepts,the
                 membership functions can be evolved simultaneously
                 within the learning process using the advantages of the
                 GA-P paradigm. Additionally,the inclusion of penalty
                 factors in the evaluation function allows us to try to
                 bias the search toward solutions that are optimal in
                 safety or economical terms,not only taking into account
                 control matters.We tested this approach with a real
                 problem,obtaining three different control policies as a
                 consequence of the different fitness functions
                 employed.So,we conclude that the manipulation of
                 fitness function and the use of a grammar to introduce
                 as much knowledge as possible into the search process
                 are useful tools when applying evolutionary techniques
                 in industrial environments.The modified fitness
                 functions and genetic operators are discussed in the
  notes =        "http://www.medwellonline.net/ijcs/",

Genetic Programming entries for Santiago Garcia Carbajal Nouhad J Rizk