Apprentissage par renforcement d'actes de communication dans un système multi-agent

Abstract

In this paper, we present a reinforcement learning approach for multi-agent communication in order to learn what to communicate, when and to whom. This method is based on introspective agents, that can reason about their own actions and data so as to construct appropriate communicative acts. We propose an extention of classical reinforcement learning algorithms with multi-agent communication. We show how communicative acts and memory can solve non-markovity and asynchronism issues MAS.