  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.
