Stochastic Control

Stochastic control focuses on optimizing decision-making in systems with inherent randomness, aiming to find optimal strategies that maximize desired outcomes despite uncertainty. Current research emphasizes efficient algorithms for solving stochastic control problems, particularly within Markov Decision Processes (MDPs) and using techniques like Monte Carlo planning, reinforcement learning (including Q-learning and policy gradient methods), and Gaussian processes to model uncertainty and learn optimal policies. These advancements are impacting diverse fields, from finance and robotics to energy management and generative modeling, by enabling more robust and efficient control of complex systems under uncertainty.