Paper Details

  • Title:

    Transfer Learning in Multi-agent Reinforcement Learning Domains

  • Author(s):

    G. Boutsioukis, I. Partalas, I. Vlahavas

  • Keywords: -
  • Abstract:

    Transfer learning refers to the process of reusing knowledge from past tasks in order to speed up the learning procedure in new tasks. In reinforcement learning, where agents often require a consider- able amount of training, transfer learning comprises a suitable solution for speeding up learning. Transfer learning methods have primarily been applied in single-agent reinforcement learning algorithms, while no prior work has addressed this issue in the case of multi-agent learning. This work proposes a novel method for transfer learning in multi-agent rein- forcement learning domains. We test the proposed approach in a multi- agent domain under various setups. The results demonstrate that the method helps to reduce the learning time and increase the asymptotic performance.

  • Category: Conference Papers
  • Tags: 2011 Boutsioukis Partalas Vlahavas