Performance Evaluation of a Multi-Agent Risk-Sensitive Tracking System

TL;DR

This paper analyzes a multi-agent risk-sensitive tracking system using LEQG models, examining how risk sensitivity and the number of agents affect system performance through analytical and simulation results.

Contribution

It provides new insights into how risk sensitivity and agent count influence multi-agent tracking performance, highlighting conditions for finite costs.

Findings

More agents improve performance under noisy or imperfect conditions

Critical risk sensitivity increases with the number of agents

A minimum number of agents is needed for finite cost at fixed risk sensitivity

Abstract

In this paper, we consider a simple linear exponential quadratic Gaussian (LEQG) tracking problem for a multi-agent system. We study the dynamical behaviors of the group as we vary the risk-sensitivity parameter, comparing in particular the risk averse case to the LQG case. Then we consider the evolution of the performance per agent as the number of agents in the system increases. We provide some analytical as well as simulation results. In general, more agents are beneficial only if noisy agent dynamics and/or imperfect measurements are considered. The critical value of the risk sensitivity parameter above which the cost becomes infinite increases with the number of agents. In other words, for a fixed positive value of this parameter, there is a minimum number of agents above which the cost remains finite.