发表机构
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多智能体轨迹规划的动态博弈方法局限，本文提出带状态依赖权重的KL正则化动态博弈及上下文感知逆向博弈模块，经模拟与多机器人实验验证了其有效性。
AI 中文摘要
本文研究了在智能体目标未知且存在依赖状态的智能体间耦合的非线性动力学系统中，多智能体轨迹规划的反馈纳什均衡（FBNE）求解问题。动态博弈理论为这类问题提供了原则性框架，但现有方法通常假设智能体完全理性且目标已知，或依赖固定正则化，限制了其在安全关键场景中捕捉有限理性和空间变化交互强度的能力。为此，我们提出了一种带有状态依赖权重的KL正则化动态博弈，该权重可自适应平衡最优性与行为先验。为从演示行为中推断未知代价参数，我们开发了一种基于最大熵逆向强化学习且带有物理信息正则化的上下文感知逆向博弈模块，确保与前向博弈的结构一致性。我们证明了正则化局部博弈的每迭代适定性，且自适应权重函数在有界标称轨迹更新下保持利普希茨连续性。针对协同导航与汇合场景的数值模拟及多机器人实验验证了所提框架的有效性。
英文摘要
This paper studies feedback Nash equilibrium (FBNE) seeking for multi-agent trajectory planning in nonlinear dynamical systems with unknown agents' objectives and state-dependent inter-agent coupling. While dynamic game theory provides a principled framework for such problems, existing approaches typically assume fully rational agents with known objectives or rely on fixed regularization, limiting their ability to capture bounded rationality and spatially varying interaction intensity in safety-critical settings. To this end, we propose a KL-regularized dynamic game with a state-dependent weight that adaptively balances optimality and behavioral priors. To infer unknown cost parameters from demonstrated behaviors, we develop a context-aware inverse game module based on maximum-entropy inverse reinforcement learning with physics-informed regularization, ensuring structural consistency with the forward game. We establish per-iteration well-posedness of the regularized local game and show that the adaptive weighting function remains Lipschitz continuous under bounded nominal-trajectory updates. Numerical simulations and multi-robot experiments on cooperative navigation and merging scenarios validate the effectiveness of the proposed framework.
