||Author(s): G. Boutsioukis, I. Partalas, I. Vlahavas.
Title: “Transfer Learning in Multi-agent Reinforcement Learning Domains”.
Click here to download the PDF (Acrobat Reader) file (12 pages).
Accepted for presentation at the 9th European Workshop on Reinforcement Learning and to be published in the Workshop Proceedings, 2011.
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