Iterative linear-quadratic (ILQ) methods are widely used in the nonlinear optimal control community. Recent work has applied similar methodology in the setting of multiplayer general-sum differential games. Here, ILQ methods are capable of finding local equilibria in interactive motion planning problems in real-time. As in most iterative procedures, however, this approach can be sensitive to initial conditions and hyperparameter choices, which can result in poor computational performance or even unsafe trajectories. In this paper, we focus our attention on a broad class of dynamical systems which are feedback linearizable, and exploit this structure to improve both algorithmic reliability and runtime. We showcase our new algorithm in three distinct traffic scenarios, and observe that in practice our method converges significantly more often and more quickly than was possible without exploiting the feedback linearizable structure.

中文翻译：

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具有反馈线性化动力学的一般和微分博弈的迭代二次方法

迭代线性二次 (ILQ) 方法广泛用于非线性最优控制社区。最近的工作在多人一般和微分游戏的设置中应用了类似的方法。在这里，ILQ 方法能够在交互式运动规划问题中实时找到局部平衡。然而，与大多数迭代过程一样，这种方法可能对初始条件和超参数选择敏感，这可能导致计算性能不佳甚至不安全的轨迹。在本文中，我们将注意力集中在一大类可反馈线性化的动态系统上，并利用这种结构来提高算法可靠性和运行时间。我们在三个不同的交通场景中展示了我们的新算法，