Interactive behavior modeling of multiple agents is an essential challenge in simulation, especially in scenarios when agents need to avoid collisions and cooperate at the same time. Humans can interact with others without explicit communication and navigate in scenarios when cooperation is required. In this work, we aim to model human interactions in this realistic setting, where each agent acts based on its observation and does not communicate with others. We propose a framework based on distributed potential games, where each agent imagines a cooperative game with other agents and solves the game using its estimation of their behavior. We utilize iLQR to solve the games and closed-loop simulate the interactions. We demonstrate the benefits of utilizing distributed imagined games in our framework through various simulation experiments. We show the high success rate, the increased navigation efficiency, and the ability to generate rich and realistic interactions with interpretable parameters. Illustrative examples are available at https://sites.google.com/berkeley.edu/distributed-interaction.

翻译：多智能体交互行为建模是仿真中的一个关键挑战，尤其是在智能体需要同时避免碰撞与协作的场景中。人类无需显式沟通即可相互交互，并在需要协作的场景中导航。在本工作中，我们旨在模拟这种现实情境下的人类交互行为：每个智能体仅基于自身观测行动，且不与其他智能体进行通信。我们提出一个基于分布式势博弈的框架，其中每个智能体想象一个与其他智能体合作的博弈，并利用自身对其他智能体行为的估计来求解该博弈。我们采用iLQR求解博弈，并闭环模拟交互过程。通过多个仿真实验，我们展示了框架中利用分布式虚拟博弈的优势，包括高成功率、导航效率提升，以及生成具有可解释参数的丰富且逼真交互行为的能力。示例演示详见https://sites.google.com/berkeley.edu/distributed-interaction。