Evolving an autonomous agent for non-Markovian reinforcement learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{DBLP:conf/gecco/JungR09,
  author =       "Jae-Yoon Jung and James A. Reggia",
  title =        "Evolving an autonomous agent for non-Markovian
                 reinforcement learning",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "971--978",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1570034",
  abstract =     "In this paper, we investigate the use of nested
                 evolution in which each step of one evolutionary
                 process involves running a second evolutionary process.
                 We apply this approach to build an evolutionary system
                 for reinforcement learning (RL) problems. Genetic
                 programming based on a descriptive encoding is used to
                 evolve the neural architecture, while an evolution
                 strategy is used to evolve the connection weights. We
                 test this method on a non-Markovian RL problem
                 involving an autonomous foraging agent, finding that
                 the evolved networks significantly outperform a
                 rule-based agent serving as a control. We also
                 demonstrate that nested evolution, partitioning into
                 subpopulations, and crossover operations all act
                 synergistically in improving performance in this
                 context.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for Jae-Yoon Jung James A Reggia

Citations