Optimal control of aging in complex networks

TL;DR

This paper develops optimal control strategies for prolonging the health and lifespan of complex interdependent networks experiencing aging, using control theory and reinforcement learning to inform maintenance protocols.

Contribution

It introduces a combined analytical and computational framework to optimize maintenance in aging complex systems, bridging control theory and machine learning.

Findings

Optimal maintenance protocols extend system longevity.

Reinforcement learning effectively identifies maintenance strategies.

Analysis reveals key factors influencing aging dynamics.

Abstract

Many complex systems experience damage accumulation which leads to aging, manifest as an increasing probability of system collapse with time. This naturally raises the question of how to maximize health and longevity in an aging system at minimal cost of maintenance and intervention. Here, we pose this question in the context of a simple interdependent network model of aging in complex systems, and use both optimal control theory and reinforcement learning alongside a combination of analysis and simulation to determine optimal maintenance protocols. These protocols may motivate the rational design of strategies for promoting longevity in aging complex systems with potential applications in therapeutic schedules and engineered system maintenance.