We introduce a multilevel reinforcement learning framework to reduce computational costs associated with model dynamics represented by coupled PDEs, offering significant savings compared to traditional methods. By leveraging sublevel models and an approximate multilevel Monte Carlo estimate, our approach demonstrates improved efficiency in solving control problems with stochastic PDE-based environments.
