We propose and study the problem of generative multi-agent behavioral cloning, where the goal is to learn a generative multi-agent policy from pre-collected demonstration data. Building upon advances in deep generative models, we present a hierarchical policy framework that can tractably learn complex mappings from input states to distributions over multi-agent action spaces. Our framework is flexible and can incorporate high-level domain knowledge into the structure of the underlying deep graphical model. For instance, we can effectively learn low-dimensional structures, such as long-term goals and team coordination, from data. Thus, an additional benefit of our hierarchical approach is the ability to plan over multiple time scales for effective long-term planning. We showcase our approach in an application of modeling team offensive play from basketball tracking data. We show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods. We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts.

本文介绍了一种用于拟合协调多智能体轨迹行为的分层生成模型框架，并利用编程生成的弱标签扩展到时空范畴，非常适合处理如篮球比赛中的复杂互动以及生成真实的多智能体轨迹。

利用编程的弱监督生成多智能体轨迹