Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI) technique. However, current studies and applications need to address its scalability, non-stationarity, and trustworthiness. This paper aims to review methods and applications and point out research trends and visionary prospects for the next decade. First, this paper summarizes the basic methods and application scenarios of MARL. Second, this paper outlines the corresponding research methods and their limitations on safety, robustness, generalization, and ethical constraints that need to be addressed in the practical applications of MARL. In particular, we believe that trustworthy MARL will become a hot research topic in the next decade. In addition, we suggest that considering human interaction is essential for the practical application of MARL in various societies. Therefore, this paper also analyzes the challenges while MARL is applied to human-machine interaction.

翻译：多智能体强化学习（MARL）是一种广泛应用的人工智能（AI）技术。然而，当前的研究与应用仍需应对其可扩展性、非平稳性及可信性等挑战。本文旨在综述MARL的方法与应用，并指出未来十年的研究趋势与前瞻性愿景。首先，本文总结了MARL的基本方法与应用场景。其次，本文概述了在MARL实际应用中需解决的安全性、鲁棒性、泛化性及伦理约束等方面的对应研究方法及其局限性。特别地，我们认为可信MARL将成为未来十年的研究热点。此外，本文提出在各类社会实际应用中，考虑人类交互对MARL至关重要。因此，本文同时分析了MARL在人机交互场景中面临的挑战。