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 ...

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 ...

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 ...

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 ...