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Transferring Evolved Reservoir Features in Reinforcement Learning Tasks

  • Kyriakos C. Chatzidimitriou
  • Ioannis Partalas
  • Pericles A. Mitkas
  • Ioannis Vlahavas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7188)

Abstract

The major goal of transfer learning is to transfer knowledge acquired on a source task in order to facilitate learning on another, different, but usually related, target task. In this paper, we are using neuroevolution to evolve echo state networks on the source task and transfer the best performing reservoirs to be used as initial population on the target task. The idea is that any non-linear, temporal features, represented by the neurons of the reservoir and evolved on the source task, along with reservoir properties, will be a good starting point for a stochastic search on the target task. In a step towards full autonomy and by taking advantage of the random and fully connected nature of echo state networks, we examine a transfer method that renders any inter-task mappings of states and actions unnecessary. We tested our approach and that of inter-task mappings in two RL testbeds: the mountain car and the server job scheduling domains. Under various setups the results we obtained in both cases are promising.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kyriakos C. Chatzidimitriou
    • 1
    • 2
  • Ioannis Partalas
    • 3
  • Pericles A. Mitkas
    • 1
    • 2
  • Ioannis Vlahavas
    • 3
  1. 1.Dept. of Electrical & Computer EngineeringAristotle University of ThessalonikiGreece
  2. 2.Informatics and Telematics InstituteCentre for Research and TechnologyHellasGreece
  3. 3.Dept. of InformaticsAristotle University of ThessalonikiGreece

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