Adversarial Multi-Agent Target Tracking with Inexact Online Gradient Descent

Multi-agent systems are being increasingly deployed in challenging environments for performing complex tasks such as multi-target tracking, search-and-rescue, and intrusion detection. This paper formulates the generic target tracking problem as a time-varying optimization problem and puts forth an inexact online gradient descent method for solving it sequentially. The performance of the proposed algorithm is studied by characterizing its dynamic regret, a notion common to the online learning literature. Building upon the existing results, we provide improved regret rates that not only allow non-strongly convex costs but also explicating the role of the cumulative gradient error. The objective function is convex but the variable belongs to a compact domain. The efficacy of the proposed inexact gradient framework is established on a multi-agent multi-target tracking problem.