This paper investigates conditions under which stochastic dynamic programs easily reduce to static deterministic programs. The conditions, though strict, are still rich enough to aid in the solution ...

Dynamic optimization and optimal control problems form the backbone of numerous applications in engineering, economics and the natural sciences. These methodologies involve determining a time-varying ...

Near-optimization is as sensible and important as optimization for both theory and applications. This paper concerns dynamic near-optimization, or near-optimal controls, for systems governed by ...

This course covers reinforcement learning aka dynamic programming, which is a modeling principle capturing dynamic environments and stochastic nature of events. The main goal is to learn dynamic ...