Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

Common approaches for direct model predictive control (MPC) for current reference tracking
in power electronics suffer from the high computational complexity encountered when …

This work develops new algorithms with rigorous efficiency guarantees for infinite horizon
imitation learning (IL) with linear function approximation without restrictive coherence …

In this letter, we discuss the problem of optimal control for affine systems in the context of
data-driven linear programming. First, we introduce a unified framework for the fixed point …

The linear programming (LP) approach is, together with value iteration and policy iteration,
one of the three fundamental methods to solve optimal control problems in a dynamic …

This article presents an iterative data-driven algorithm for solving dynamic multiobjective
(MO) optimal control problems arising in control of nonlinear continuous-time systems. It is …

The linear programming (LP) approach has a long history in the theory of approximate
dynamic programming. When it comes to computation, however, the LP approach often …

We consider large-scale Markov decision processes with an unknown cost function and
address the problem of learning a policy from a finite set of expert demonstrations. We …

We consider the problem of learning discounted-cost optimal control policies for unknown
deterministic discrete-time systems with continuous state and action spaces. We show that a …

Abstract Model-based predictive controllers are used to tackle control tasks in which
constraints on state, input or both need to be satisfied. These controllers commonly optimize …