This paper serves to introduce the reader to the field of multi-agent reinforcement learning (MARL) and its intersection with methods from the study of causality. We highlight key challenges in MARL and discuss these in the context of how causal methods may assist in tackling them. We promote moving toward a 'causality first' perspective on MARL. Specifically, we argue that causality can offer improved safety, interpretability, and robustness, while also providing strong theoretical guarantees for emergent behaviour. We discuss potential solutions for common challenges, and use this context to motivate future research directions.

翻译：本文向读者介绍多试剂强化学习领域及其与因果关系研究方法的交汇情况。我们着重介绍MARL的主要挑战,并结合因果方法如何有助于解决这些问题来讨论这些挑战。我们推动从“因果第一”的角度看待MARL。具体地说,我们主张,因果可以改善安全、可解释性和稳健性,同时也为新出现的行为提供强有力的理论保障。我们讨论了共同挑战的潜在解决办法,并利用这一背景来激励未来的研究方向。