Transfer Learning in Multi-Agent Reinforcement Learning Domains

  • Georgios Boutsioukis
  • Ioannis Partalas
  • Ioannis Vlahavas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7188)


In the context of reinforcement learning, transfer learning refers to the concept of reusing knowledge acquired in past tasks to speed up the learning procedure in new tasks. Transfer learning methods have been succesfully applied in single-agent reinforcement learning algorithms, but no prior work has focused on applying them in a multi-agent environment. We propose a novel method for transfer learning in multi-agent reinforcement learning domains. We proceed to test the proposed approach in a multi-agent domain under various configurations. The results demonstrate that the method can reduce the learning time and increase the asymptotic performance of the learning algorithm.


Reinforcement Learn Multiagent System Transfer Learning Target Task Direct Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Georgios Boutsioukis
    • 1
  • Ioannis Partalas
    • 1
  • Ioannis Vlahavas
    • 1
  1. 1.Department of InformaticsAristotle UniversityThessalonikiGreece

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