This research explores the application of reinforcement learning to adaptively couple full and reduced order models in hybrid simulations, particularly in transient problems where localized features propagate through the domain. The approach uses deep Q-networks to select between subdomain-local full order models and pre-trained operator inference reduced order models to balance accuracy, cost, and model-switching frequency.