ALGAMES: A Fast Augmented Lagrangian Solver for Constrained Dynamic Games

TL;DR

ALGAMES is a fast, robust solver for constrained dynamic games that efficiently handles multi-actor trajectory optimization, demonstrated in autonomous driving scenarios with significant interaction complexity.

Contribution

It introduces a novel augmented Lagrangian-based solver using a quasi-Newton method for constrained dynamic games, achieving faster solutions than existing approaches.

Findings

Solves complex autonomous driving scenarios three times faster than DDP-based methods.

Operates at over 60 Hz in model-predictive control settings.

Effectively mitigates the 'frozen robot' problem in crowded merging scenarios.

Abstract

Dynamic games are an effective paradigm for dealing with the control of multiple interacting actors. This paper introduces ALGAMES (Augmented Lagrangian GAME-theoretic Solver), a solver that handles trajectory-optimization problems with multiple actors and general nonlinear state and input constraints. Its novelty resides in satisfying the first-order optimality conditions with a quasi-Newton root-finding algorithm and rigorously enforcing constraints using an augmented Lagrangian method. We evaluate our solver in the context of autonomous driving on scenarios with a strong level of interactions between the vehicles. We assess the robustness of the solver using Monte Carlo simulations. It is able to reliably solve complex problems like ramp merging with three vehicles three times faster than a state-of-the-art DDP-based approach. A model-predictive control (MPC) implementation of the algorithm, running at more than 60 Hz, demonstrates ALGAMES' ability to mitigate the "frozen robot" problem on complex autonomous driving scenarios like merging onto a crowded highway.