带约束势手术的博弈论控制
Game-Theoretic Control with Constrained Potential Surgery
浏览论文内容
中文总结 AI 辅助
针对约束动态博弈求解速度慢及易陷入非纳什鞍点的问题，提出带二阶校正的内点求解器，提升收敛到局部GNE的概率，并经数值与赛车实验验证。
中文摘要 AI 辅助
约束一般和动态博弈是高度交互式多智能体规划问题的一种流行表述。近年来，广义纳什均衡（GNE）求解器已能在小型动态博弈中实现实时性能。然而，求解速度仍然是瓶颈，在动态任务中控制即使少量智能体（例如超过四个）仍然难以实现。此外，专注于一阶条件的牛顿求解器容易陷入非纳什鞍点，限制了所提供解的有效性。在这项工作中，我们提出了一种快速且通用的约束动态博弈内点求解器，并配以计算高效的二阶校正，该校正增加了在约束动态博弈中收敛到局部GNE解的概率。所提出方法的性能在数值基准测试和涉及缩比赛车的物理实验中进行了评估。
英文摘要
Constrained general-sum dynamic games are a popular formulation for highly interactive multi-agent planning problems. In recent years, Generalized Nash Equilibrium (GNE) solvers have achieved real-time performance for small dynamic games. However, solution speed still remains a bottleneck, and controlling even a small number of agents (e.g., more than four) in a dynamic task remains elusive. In addition, Newton solvers that focus on the first-order conditions are vulnerable to non-Nash saddle points, limiting the usefulness of the provided solution. In this work, we propose a fast and versatile interior point solver for constrained dynamic games, along with a computationally efficient second-order correction that increases the probability of converging to a local GNE solution in constrained dynamic games. The performance of the proposed method is evaluated on numerical benchmarks and a physical experiment involving scaled race cars.
