Recent Developments in Machine Learning Methods for Stochastic Control and Games

arXiv:2303.10257v2 Announce Type: replace-cross
Abstract: Stochastic optimal control and games have a wide range of applications, from finance and economics to social sciences, robotics, and energy management. Many real-world applications involve complex models that have driven the development of sophisticated numerical methods. Recently, computational methods based on machine learning have been developed for solving stochastic control problems and games. In this review, we focus on deep learning methods that have unlocked the possibility of solving such problems, even in high …

abstract applications arxiv computational control cs.lg development economics energy energy management finance games machine machine learning management math.oc numerical robotics social social sciences stochastic type world