DeepAI AI Chat
Log In Sign Up

An Iterative Quadratic Method for General-Sum Differential Games with Feedback Linearizable Dynamics

10/01/2019
by   David Fridovich-Keil, et al.
berkeley college
0

Iterative linear-quadratic (ILQ) methods are widely used in the nonlinear optimal control community. Recent work has applied similar methodology in the setting of multi-player general-sum differential games. Here, ILQ methods are capable of finding local Nash 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.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/10/2021

Potential iLQR: A Potential-Minimizing Controller for Planning Multi-Agent Interactive Trajectories

Many robotic applications involve interactions between multiple agents w...
05/31/2019

Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games

We study the global convergence of policy optimization for finding the N...
02/24/2020

iLQGames.jl: Rapidly Designing and Solving Differential Games in Julia

In many problems that involve multiple decision making agents, optimal c...
10/14/2019

Learning to Correlate in Multi-Player General-Sum Sequential Games

In the context of multi-player, general-sum games, there is an increasin...
11/01/2020

Approximate Solutions to a Class of Reachability Games

In this paper, we present a method for finding approximate Nash equilibr...
03/04/2021

Differential Flatness as a Sufficient Condition to Generate Optimal Trajectories in Real Time

As robotic systems increase in autonomy, there is a strong need to plan ...