| Authors |
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| Publication date |
2008
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| Journal |
BNAIC
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| Event |
Twentieth Belgian-Netherlands Conference on Artificial Intelligence (BNAIC 2008), Enschede, the Netherlands
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| Volume | Issue number |
20
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| Pages (from-to) |
241-248
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| Organisations |
-
Faculty of Science (FNWI) - Informatics Institute (IVI)
|
| Abstract |
In this paper we propose a method for multi-agent reinforcement learning by automatic discovery of abstract trajectories. Local details are abstracted from successful trajectories and the resulting generalized, abstract trajectories are exchanged between agents. Each agent learns a policy for its own environment. By abstracting trajectories and sharing the result the agents benefit from each others learning. This reduces the overall learning time compared to individual learning.
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| Document type |
Article
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| Note |
Proceedings title: BNAIC 2008: Belgian-Dutch Conference on Artificial Intelligence: proceedings of the twentieth Belgian-Dutch Conference on Artificial Intelligence: Enschede, October 30-31, 2008
Publisher: Universiteit Twente, Faculteit Elektrotechniek, Wiskunde en Informatica
Place of publication: Enschede
Editors: A. Nijholt, M. Pantic, M. Poel, H. Hondorp
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| Language |
English
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| Published at |
http://eprints.eemcs.utwente.nl/13354/
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