LAUSR

Many real-world tasks can be cast into multiagent (MA) reinforcement learning problems, and most algorithms in this field obey to the centralized learning and decentralized execution framework. However, enforcing centralized learning is impractical in many scenarios. Because it requires integrating the information from agents, while agents may not hope to share local information due to the issue of privacy. Thus, this article proposes a novel approach to achieve fully decentralized learning based on communication among multiple agents via reinforcement learning. Benefiting from causality analysis, an agent will choose the counterfactual that has the most significant influence on communication information of others. We find that this method can be applied in classic or complex MA scenarios and in federated learning domains, which are now attracting much attention.