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