Apprentissage par Renforcement et Théorie des Jeux pour la coordination de Systèmes Multi-Agents
Résumé
This article presents the main reinforcement learning algorithms that aim at coordinating multi-agent systems by using tools and formalisms borrowed from Game Theory. Limits of these approaches are studied and discussed in order to draw some promising lines of research for that particular field. We argue more deeply around the central notions of Nash equilibrium and games with imperfect monitoring.
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