Density control of interacting agent systems

TL;DR

This paper introduces a novel optimal control algorithm for large interacting agent systems, explicitly modeling interactions and extending to multi-species scenarios, with proven convergence and practical numerical demonstrations.

Contribution

The paper presents a new feedback control method based on optimal transport theory that explicitly models agent interactions and extends to multi-species systems.

Findings

Algorithm guarantees convergence with sublinear rate.

Framework successfully applied to multi-species agent systems.

Numerical examples demonstrate effectiveness of the control strategy.

Abstract

We consider the problem of controlling the group behavior of a large number of dynamic systems that are constantly interacting with each other. These systems are assumed to have identical dynamics (e.g., birds flock, robot swarm) and their group behavior can be modeled by a distribution. Thus, this problem can be viewed as an optimal control problem over the space of distributions. We propose a novel algorithm to compute a feedback control strategy so that, when adopted by the agents, the distribution of them would be transformed from an initial one to a target one over a finite time window. Our method is built on optimal transport theory but differs significantly from existing work in this area in that our method models the interactions among agents explicitly. From an algorithmic point of view, our algorithm is based on a generalized version of the proximal gradient descent algorithm and has a convergence guarantee with a sublinear rate. We further extend our framework to account for the scenarios where the agents are from multiple species. In the linear quadratic setting, the solution is characterized by coupled Riccati equations which can be solved in closed-form. Finally, several numerical examples are presented to illustrate our framework.