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An Iterative Quadratic Method for General-Sum Differential Games with Feedback Linearizable Dynamics
arXiv - CS - Computer Science and Game Theory Pub Date : 2019-10-01 , DOI: arxiv-1910.00681 David Fridovich-Keil, Vicenc Rubies-Royo, and Claire J. Tomlin
arXiv - CS - Computer Science and Game Theory Pub Date : 2019-10-01 , DOI: arxiv-1910.00681 David Fridovich-Keil, Vicenc Rubies-Royo, and Claire J. Tomlin
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:
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,
更新日期:2020-03-20
中文翻译:
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,