Learning to weigh basic behaviors in Scalable Agents

Olivier Buffet 1 Alain Dutech 1 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Agents, especially in the context of Multi-Agents Systems, are confronted to complex tasks. We propose a methodology for the automated design of such agents in the case where the global task can be decomposed into simpler sub-tasks that can be concurrent. This is accomplished by automatically combining basic behaviors using Reinforcement Learning methods. Basic behaviors are either learned or reused from previous tasks as they do not need to be tuned to the specific task being learned. Furthermore, the agents designed by our methodology are highly scalable as, without further refinement of the global behavior, they can automatically combine several instances of the same basic behavior to take into account concurrent occurences of the same subtask.
Mots-clés : multiagent systems reinforcement learning scalability adaptation complex environments mise à l'échelle environnements complexes systèmes multi-agents apprentissage par renforcement
Type de document :
Communication dans un congrès
First International Joint Conference on Autonomous Agents and Multiagent Systems - AAMAS 2002, 2002, Bologna, Italy, 3, pp.1264-1265, 2002
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https://hal.inria.fr/inria-00100765
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 14:50:25
Dernière modification le : jeudi 11 janvier 2018 - 06:19:50

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  • HAL Id : inria-00100765, version 1

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Olivier Buffet, Alain Dutech, François Charpillet. Learning to weigh basic behaviors in Scalable Agents. First International Joint Conference on Autonomous Agents and Multiagent Systems - AAMAS 2002, 2002, Bologna, Italy, 3, pp.1264-1265, 2002. 〈inria-00100765〉

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