Scalable Forward Reachability Analysis of Multi-Agent Systems with Neural Network Controllers

TL;DR

This paper presents a scalable method for computing overapproximations of forward reachable sets in multi-agent systems with neural network controllers, improving robustness analysis efficiency.

Contribution

It introduces a reformulation of system dynamics and leverages distributed architecture to reduce computational complexity using semidefinite programming.

Findings

Method successfully scales to large multi-agent systems.

Overapproximations verified on vehicle platoon and power network examples.

Significant reduction in computation time compared to existing methods.

Abstract

Neural networks (NNs) have been shown to learn complex control laws successfully, often with performance advantages or decreased computational cost compared to alternative methods. Neural network controllers (NNCs) are, however, highly sensitive to disturbances and uncertainty, meaning that it can be challenging to make satisfactory robustness guarantees for systems with these controllers. This problem is exacerbated when considering multi-agent NN-controlled systems, as existing reachability methods often scale poorly for large systems. This paper addresses the problem of finding overapproximations of forward reachable sets for discrete-time uncertain multi-agent systems with distributed NNC architectures. We first reformulate the dynamics, making the system more amenable to reachablility analysis. Next, we take advantage of the distributed architecture to split the overall reachability problem into smaller problems, significantly reducing computation time. We use quadratic constraints, along with a convex representation of uncertainty in each agent's model, to form semidefinite programs, the solutions of which give overapproximations of forward reachable sets for each agent. Finally, the methodology is tested on two realistic examples: a platoon of vehicles and a power network system.