Publication Details
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Author(s): G. Boutsioukis, I. Partalas, I. Vlahavas.
Title: “Transfer Learning in Multi-agent Reinforcement Learning Domains”.
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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
performance.
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