A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria

TL;DR

This paper introduces a sequential quadratic programming method for efficiently computing local generalized Nash equilibria in open-loop dynamic games with nonlinear dynamics, demonstrating improved convergence and success rates in a car racing scenario.

Contribution

The paper presents a novel SQP-based numerical method for solving local GNE in nonlinear dynamic games, with a robust line search and merit function for better convergence.

Findings

Achieves linear convergence near GNE

Up to 32% improvement in success rate in car racing simulations

Requires only solving a single convex quadratic program per iteration

Abstract

Dynamic games can be an effective approach to modeling interactive behavior between multiple non-cooperative agents and they provide a theoretical framework for simultaneous prediction and control in such scenarios. In this work, we propose a numerical method for the solution of local generalized Nash equilibria (GNE) for the class of open-loop general-sum dynamic games for agents with nonlinear dynamics and constraints. In particular, we formulate a sequential quadratic programming (SQP) approach which requires only the solution of a single convex quadratic program at each iteration. Central to the robustness of our approach is a non-monotonic line search method and a novel merit function for SQP step acceptance. We show that our method achieves linear convergence in the neighborhood of local GNE and we derive an update rule for the merit function which helps to improve convergence from a larger set of initial conditions. We demonstrate the effectiveness of the algorithm in the context of car racing, where we show up to 32\% improvement of success rate when comparing against a state-of-the-art solution approach for dynamic games. \url{https://github.com/zhu-edward/DGSQP}.