We propose the policy graph as a structured way of formulating a general class of multistage
stochastic programming problems in a way that leads to a natural decomposition. We also …

Hybrid model predictive control (MPC) with both continuous and discrete variables is widely
applicable to robotic control tasks, especially those involving contact with the environment …

In some applications the considered multistage stochastic programs have a periodical
behavior. We show that in such cases it is possible to drastically reduce the number of …

We consider a rental service with a fixed number of rental units distributed across multiple
locations. The units are accessed by customers without prior reservation and on an on …

Hybrid model predictive control with both continuous and discrete variables is widely
applicable to robotic control tasks, especially those involving contact with the environment …

Hybrid model predictive control with both continuous and discrete variables is widely
applicable to robotics tasks. Due to the combinatorial complexity, the solving speed of hybrid …

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 …

We consider stochastic dynamic programming problems with high-dimensional, discrete
state-spaces and finite, discrete-time horizons that prohibit direct computation of the value …

This paper presents a model-free reinforcement learning (RL) algorithm to solve the risk-
averse optimal control (RAOC) problem for discrete-time nonlinear systems. While …

We consider dynamic programming problems with finite, discrete-time horizons and
prohibitively high-dimensional, discrete state-spaces for direct computation of the value …