- Online collaborative multi-agent reinforcement learning by transfer of abstract trajectories
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
- 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.
- 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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