Multi-Agent Systems by Incremental Gradient Reinforcement Learning.

Alain Dutech 1 Olivier Buffet 1 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : A new reinforcement learning (RL) methodology is proposed to design multi-agent systems. In the realistic setting of situated agents with local perception, the task of automatically building a coordinated system is of crucial importance. We use simple reactive agents which learn their own behavior in a decentralized way. To cope with the difficulties inherent to RL used in that framework, we have developed an incremental learning algorithm where agents face more and more complex tasks. We illustrate this general framework on a computer experiment where agents have to coordinate to reach a global goal.
Type de document :
Communication dans un congrès
17th International Joint Conference on Artificial Intelligence, 2001, Seattle, WA, USA, 2, pp.833--838, 2001
Liste complète des métadonnées

https://hal.inria.fr/inria-00101090
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 14:56:27
Dernière modification le : jeudi 11 janvier 2018 - 06:19:50

Identifiants

  • HAL Id : inria-00101090, version 1

Collections

Citation

Alain Dutech, Olivier Buffet, François Charpillet. Multi-Agent Systems by Incremental Gradient Reinforcement Learning.. 17th International Joint Conference on Artificial Intelligence, 2001, Seattle, WA, USA, 2, pp.833--838, 2001. 〈inria-00101090〉

Partager

Métriques

Consultations de la notice

223