We describe a nonlinear generalization of dual dynamic programming (DP) theory and its
application to value function estimation for deterministic control problems over continuous …

We present a finite-horizon optimization algorithm that extends the established concept of
Dual Dynamic Programming (DDP) in two ways. First, in contrast to the linear costs …

Q-functions are widely used in discrete-time learning and control to model future costs
arising from a given control policy, when the initial state and input are given. Although some …

In this article, we describe an approximate dynamic programming (ADP) approach to
compute lower bounds on the optimal value function for a discrete time, continuous space …

While Approximate Dynamic Programming has successfully been used in many applications
involving discrete states and inputs such as playing the games of Tetris or chess, it has not …

Stochastic optimal control problems are generally difficult to solve to optimality, and it is
desirable to be able to transfer a high-quality solution learned for one such problem to a …

This thesis studies polynomial optimization techniques to reduce operational cost and
carbon emissions of electrical and thermal energy systems. Based on recent results from …