We introduce MAgent, a platform to support research and development of
many-agent reinforcement learning. Unlike previous research platforms on single
or multi-agent reinforcement learning, MAgent focuses on supporting the tasks
and the applications that require hundreds to millions of agents. Within the
interactions among a population of agents, it enables not only the study of
learning algorithms for agents' optimal polices, but more importantly, the
observation and understanding of individual agent's behaviors and social
phenomena emerging from the AI society, including communication languages,
leaderships, altruism. MAgent is highly scalable and can host up to one million
agents on a single GPU server. MAgent also provides flexible configurations for
AI researchers to design their customized environments and agents. In this
demo, we present three environments designed on MAgent and show emerged
collective intelligence by learning from scratch.

MAgent 是一个高度可扩展和可定制的平台，支持多智能体强化学习任务和应用程序，并提供可观察和理解个体智能体行为和社会现象的功能，此演示以三个环境为例展示了从零开始学习的集体智能的表现。