A gradient-enhanced functional tensor train cross approximation method for the resolution of
the Hamilton–Jacobi–Bellman (HJB) equations associated with optimal feedback control of …

Recent research shows that supervised learning can be an effective tool for designing near-
optimal feedback controllers for high-dimensional nonlinear dynamic systems. But the …

This work is concerned with solving neural network-based feedback controllers efficiently for
optimal control problems. We first conduct a comparative study of two prevalent approaches …

We propose an actor-critic framework to solve the time-continuous stochastic optimal control
problem. A least square temporal difference method is applied to compute the value function …

Mean-field control (MFC) problems aim to find the optimal policy to control massive
populations of interacting agents. These problems are crucial in areas such as economics …

Designing optimal feedback controllers for nonlinear dynamical systems requires solving
Hamilton-Jacobi-Bellman equations, which are notoriously difficult when the state dimension …

A deep learning approach for the approximation of the Hamilton-Jacobi-Bellman partial
differential equation (HJB PDE) associated to the Nonlinear Quadratic Regulator (NLQR) …

Optimal control problems driven by evolutionary partial differential equations arise in many
industrial applications and their numerical solution is known to be a challenging problem …

Robust control design for quantum systems is a challenging and key task for practical
technology. In this work, we apply neural networks to learn the control problem for the …

This work proposes novel Neural Network (NN) training algorithms and architectures to
solve with lowcost general Optimal Control problems: regression of the optimal control policy …