Stochastic Optimal Control

Stochastic optimal control (SOC) focuses on finding the best way to control a system that evolves randomly over time, aiming to maximize a desired outcome. Current research emphasizes developing efficient, simulation-free algorithms, often leveraging deep learning architectures like neural networks and employing techniques such as adjoint matching and path integral methods to solve the underlying optimization problems. These advancements are significantly impacting fields like generative modeling, robotics, and finance by enabling more efficient and robust control of complex systems with high-dimensional state spaces and uncertainties. The development of scalable and theoretically sound methods for handling various types of uncertainty, including distributional robustness, is a key focus.