In multi-agent systems, complex interacting behaviors arise due to the high
correlations among agents. However, previous work on modeling multi-agent
interactions from demonstrations is primarily constrained by assuming the
independence among policies and their reward structures. In this paper, we cast
the multi-agent interactions modeling problem into a multi-agent imitation
learning framework with explicit modeling of correlated policies by
approximating opponents' policies, which can recover agents' policies that can
regenerate similar interactions. Consequently, we develop a Decentralized
Adversarial Imitation Learning algorithm with Correlated policies (CoDAIL),
which allows for decentralized training and execution. Various experiments
demonstrate that CoDAIL can better regenerate complex interactions close to the
demonstrators and outperforms state-of-the-art multi-agent imitation learning
methods. Our code is available at https://github.com/apexrl/CoDAIL.

本文提出了一种基于相互关联策略的多智能体系统建模与仿真方法，并通过去近似敌方策略恢复代理策略的方法，设计了一种去中心化对抗性学习算法，实现了模型的训练和执行，实验证明了该算法的优越性，代码可在 https://github.com/apexrl/CoDAIL 中获取。