Imitation learning (IL) seeks to teach agents specific tasks through expert
demonstrations. One of the key approaches to IL is to define a distance between
agent and expert and to find an agent policy that minimizes that distance.
Optimal transport methods have been widely used in imitation learning as they
provide ways to measure meaningful distances between agent and expert
trajectories. However, the problem of how to optimally combine multiple expert
demonstrations has not been widely studied. The standard method is to simply
concatenate state (-action) trajectories, which is problematic when
trajectories are multi-modal. We propose an alternative method that uses a
multi-marginal optimal transport distance and enables the combination of
multiple and diverse state-trajectories in the OT sense, providing a more
sensible geometric average of the demonstrations. Our approach enables an agent
to learn from several experts, and its efficiency is analyzed on OpenAI Gym
control environments and demonstrates that the standard method is not always
optimal.

使用多边际最优传输距离的替代方法，实现了在 OT 意义下多个和多样化状态轨迹的组合，提供了更合理的演示几何平均值，从而使代理从多个专家中学习，并在 OpenAI Gym 控制环境中进行了效率分析，表明标准方法并不总是最优的。