  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.
