EVM: Lifelong reinforcement and self-learning

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

@InProceedings{Nowostawski:2009:IMCSIT,
  author =       "Mariusz Nowostawski",
  title =        "EVM: Lifelong reinforcement and self-learning",
  booktitle =    "International Multiconference on Computer Science and
                 Information Technology, IMCSIT '09",
  year =         "2009",
  month =        oct,
  pages =        "89--98",
  publisher =    "IEEE ?",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.proceedings2009.imcsit.org/pliks/iv_imcsit.pdf",
  abstract =     "Open-ended systems and unknown dynamical environments
                 present challenges to the traditional machine learning
                 systems, and in many cases traditional methods are not
                 applicable. Lifelong reinforcement learning is a
                 special case of dynamic (process-oriented)
                 reinforcement learning. Multi-task learning is a
                 methodology that exploits similarities and patterns
                 across multiple tasks. Both can be successfully used
                 for open-ended systems and automated learning in
                 unknown environments. Due to its unique
                 characteristics, lifelong reinforcement presents both
                 challenges and potential capabilities that go beyond
                 traditional reinforcement learning methods. In this
                 article, we present the basic notions of lifelong
                 reinforcement learning, introduce the main
                 methodologies, applications and challenges. We also
                 introduce a new model of lifelong reinforcement based
                 on the Evolvable Virtual Machine architecture (EVM).",
  notes =        "Information Science Department Otago University PO Box
                 56 Dunedin, New Zealand",
}

Genetic Programming entries for Mariusz Nowostawski

Citations