Hybridising evolution and temporal difference learning

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

@PhdThesis{Burrow:thesis,
  author =       "Peter Richard Burrow",
  title =        "Hybridising evolution and temporal difference
                 learning",
  school =       "University of Essex",
  year =         "2011",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.572783",
  abstract =     "This work investigates combinations of two different
                 nature-inspired machine learning algorithms -
                 Evolutionary Algorithms and Temporal Difference
                 Learning. Both algorithms are introduced along with a
                 survey of previous work in the field. A variety of ways
                 of hybridising the two algorithms are considered,
                 falling into two main categories - those where both
                 algorithms operate on the same set of parameters, and
                 those where evolution searches for beneficial
                 parameters to aid Temporal Difference Learning. These
                 potential approaches to hybridisation are explored by
                 applying them to three different problem domains, all
                 loosely linked by the theme of games. The Mountain Car
                 task is a common reinforcement learning benchmark that
                 has been shown to be potentially problematic for neural
                 networks. Ms. Pac-Man is a classic arcade game with a
                 complex virtual environment, and Othello is a popular
                 two-player zero sum board game. Results show that
                 simple hybridisation approaches often do not improve
                 performance, which can be dependent on many factors of
                 the individual algorithms. However, results have also
                 shown that these factors can be successfully tuned by
                 evolution. The main contributions of this thesis are an
                 analysis of the factors that can affect individual
                 algorithm performance, and demonstration of some novel
                 approaches to hybridisation. These consist of use of
                 Evolution Strategies to tune Temporal Difference
                 Learning parameters on multiple problem domains, and
                 evolution of n-tuple configurations for Othello board
                 evaluation. In the latter case, a level of performance
                 was achieved that was competitive with the state of the
                 art.",
  notes =        "Is this GP? EThOS ID: uk.bl.ethos.572783",
}

Genetic Programming entries for Peter Burrow

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