Dynamic game theory is a powerful tool in modeling multi-agent interactions and human-robot systems. In practice, since the objective functions of both agents may not be explicitly known to each other, these interactions can be modeled as incomplete-information general-sum dynamic games. Solving for equilibrium policies for such games presents a major challenge, especially if the games involve nonlinear underlying dynamics. To simplify the problem, existing work often assumes that one agent is an expert with complete information about its peer, which can lead to biased estimates and failures in coordination. To address this challenge, we propose a nonlinear peer-aware cost estimation (N-PACE) algorithm for general-sum dynamic games. In N-PACE, using iterative linear quadratic (ILQ) approximation of dynamic games, each agent explicitly models the learning dynamics of its peer agent while inferring their objective functions and updating its own control policy accordingly in real time, which leads to unbiased and fast learning of the unknown objective function of the peer agent. Additionally, we demonstrate how N-PACE enables intent communication by explicitly modeling the peer's learning dynamics. Finally, we show how N-PACE outperforms baseline methods that disregard the learning behavior of the other agent, both analytically and using our case studies

翻译：动态博弈论是多智能体交互与人机系统建模的有力工具。在实际应用中，由于双方智能体的目标函数可能无法被对方明确获知，这类交互可建模为不完全信息一般和动态博弈。为此类博弈求解均衡策略面临重大挑战，尤其在博弈涉及非线性底层动力学时。为简化问题，现有工作通常假设某一智能体是具有完全同伴信息的专家，这可能导致估计偏差与协调失败。为应对这一挑战，我们提出了一种用于一般和动态博弈的非线性同伴感知代价估计算法。在该算法中，通过动态博弈的迭代线性二次逼近，每个智能体在推断同伴目标函数并实时更新自身控制策略的同时，显式建模其同伴智能体的学习动态，从而实现对同伴未知目标函数的无偏快速学习。此外，我们通过显式建模同伴的学习动态，展示了该算法如何实现意图通信。最后，我们通过理论分析与案例研究证明，该算法在性能上优于忽略其他智能体学习行为的基线方法。