  We tackle the challenge of large-scale network intervention for guiding
excitatory point processes, such as infectious disease spread or traffic
congestion control. Our model-based reinforcement learning utilizes neural ODEs
to capture how the networked excitatory point processes will evolve subject to
the time-varying changes in network topology. Our approach incorporates
Gradient-Descent based Model Predictive Control (GD-MPC), offering policy
flexibility to accommodate prior knowledge and constraints. To address the
intricacies of planning and overcome the high dimensionality inherent to such
decision-making problems, we design an Amortize Network Interventions (ANI)
framework, allowing for the pooling of optimal policies from history and other
contexts, while ensuring a permutation equivalent property. This property
enables efficient knowledge transfer and sharing across diverse contexts. Our
approach has broad applications, from curbing infectious disease spread to
reducing carbon emissions through traffic light optimization, and thus has the
potential to address critical societal and environmental challenges.

Machine Learning