The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predictions of agents' future behavior in multi-agent environments. Because these models operate on the discrete entities and relations present in the environment, they produce interpretable intermediate representations which offer insights into what drives agents' behavior, and what events mediate the intensity and valence of social interactions. Furthermore, we show that embedding RFM modules inside agents results in faster learning systems compared to non-augmented baselines. As more and more of the autonomous systems we develop and interact with become multi-agent in nature, developing richer analysis tools for characterizing how and why agents make decisions is increasingly necessary. Moreover, developing artificial agents that quickly and safely learn to coordinate with one another, and with humans in shared environments, is crucial.

介绍了一种利用关系前向模型（RFM）来学习多智能体环境变化，预测代理机器的未来行为的算法。在多智能体环境中，这些模型产生可解释的中间表示，并提供对推动代理行为的因素、调节社交互动强度和价值的事件的见解。此外，将 RFM 模块嵌入代理中可导致更快的学习系统。

多智能体学习的关系前向模型