通过逆博弈实现交互策略的结构化模仿学习
Structured Imitation Learning of Interactive Policies through Inverse Games
AI总结：
针对共享空间中无显式通信的多智能体交互策略模仿学习难题，提出结合生成式单智能体策略学习与博弈论结构的框架，通过两步学习提升性能，仅50次演示即达真实策略水平。
摘要：
基于生成模型的模仿学习方法最近在从人类演示中学习高复杂度运动技能方面取得了显著成果。然而，在无显式通信的共享空间中与人类协作的交互策略的模仿学习仍具挑战性，因为多智能体交互中的行为复杂度远高于非交互任务。本工作将生成式单智能体策略学习与灵活且具表达性的博弈论结构相结合，提出一种交互策略的结构化模仿学习框架。该方法明确将学习分为两步：首先，利用标准模仿学习从多智能体演示中学习个体行为模式；然后，通过求解逆博弈问题结构化学习智能体间的依赖关系。在合成5智能体社交导航任务中的初步结果显示，该方法显著提升了非交互策略的性能，仅用50次演示即可达到与真实交互策略相当的表现。这些结果凸显了结构化模仿学习在交互场景中的潜力。
英文摘要：
Generative model-based imitation learning methods have recently achieved strong results in learning high-complexity motor skills from human demonstrations. However, imitation learning of interactive policies that coordinate with humans in shared spaces without explicit communication remains challenging, due to the significantly higher behavioral complexity in multi-agent interactions compared to non-interactive tasks. In this work, we introduce a structured imitation learning framework for interactive policies by combining generative single-agent policy learning with a flexible yet expressive game-theoretic structure. Our method explicitly separates learning into two steps: first, we learn individual behavioral patterns from multi-agent demonstrations using standard imitation learning; then, we structurally learn inter-agent dependencies by solving an inverse game problem. Preliminary results in a synthetic 5-agent social navigation task show that our method significantly improves non-interactive policies and performs comparably to the ground truth interactive policy using only 50 demonstrations. These results highlight the potential of structured imitation learning in interactive settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
