Data-driven initialization of deep learning solvers for Hamilton-Jacobi-Bellman PDEs
A deep learning approach for the approximation of the Hamilton-Jacobi-Bellman partial
differential equation (HJB PDE) associated to the Nonlinear Quadratic Regulator (NLQR) …
differential equation (HJB PDE) associated to the Nonlinear Quadratic Regulator (NLQR) …
Adaptive deep learning for high-dimensional Hamilton--Jacobi--Bellman equations
Computing optimal feedback controls for nonlinear systems generally requires solving
Hamilton--Jacobi--Bellman (HJB) equations, which are notoriously difficult when the state …
Hamilton--Jacobi--Bellman (HJB) equations, which are notoriously difficult when the state …
A causality-free neural network method for high-dimensional Hamilton-Jacobi-Bellman equations
T Nakamura-Zimmerer, Q Gong… - 2020 American Control …, 2020 - ieeexplore.ieee.org
Computing optimal feedback controls for nonlinear systems generally requires solving
Hamilton-Jacobi-Bellman (HJB) equations, which, in high dimensions, are notoriously …
Hamilton-Jacobi-Bellman (HJB) equations, which, in high dimensions, are notoriously …
QRnet: Optimal regulator design with LQR-augmented neural networks
T Nakamura-Zimmerer, Q Gong… - IEEE Control Systems …, 2020 - ieeexplore.ieee.org
In this letter we propose a new computational method for designing optimal regulators for
high-dimensional nonlinear systems. The proposed approach leverages physics-informed …
high-dimensional nonlinear systems. The proposed approach leverages physics-informed …
Hamilton-Jacobi Based Policy-Iteration via Deep Operator Learning
The framework of deep operator network (DeepONet) has been widely exploited thanks to
its capability of solving high dimensional partial differential equations. In this paper, we …
its capability of solving high dimensional partial differential equations. In this paper, we …
Actor-critic method for high dimensional static Hamilton--Jacobi--Bellman partial differential equations based on neural networks
We propose a novel numerical method for high dimensional Hamilton--Jacobi--Bellman
(HJB) type elliptic partial differential equations (PDEs). The HJB PDEs, reformulated as …
(HJB) type elliptic partial differential equations (PDEs). The HJB PDEs, reformulated as …
SOC-MartNet: A Martingale Neural Network for the Hamilton-Jacobi-Bellman Equation without Explicit inf H in Stochastic Optimal Controls
W Cai, S Fang, T Zhou - arXiv preprint arXiv:2405.03169, 2024 - arxiv.org
In this work, we propose a martingale based neural network, SOC-MartNet, for solving high-
dimensional Hamilton-Jacobi-Bellman (HJB) equations where no explicit expression is …
dimensional Hamilton-Jacobi-Bellman (HJB) equations where no explicit expression is …
Algorithms of data generation for deep learning and feedback design: A survey
Recent research reveals that deep learning is an effective way of solving high dimensional
Hamilton–Jacobi–Bellman equations. The resulting feedback control law in the form of a …
Hamilton–Jacobi–Bellman equations. The resulting feedback control law in the form of a …
Deep reinforcement learning based finite-horizon optimal control for a discrete-time affine nonlinear system
Approximate dynamic programming (ADP) aims to obtain an approximate numerical solution
to the discrete-time Hamilton-Jacobi-Bellman (HJB) equation. Heuristic dynamic …
to the discrete-time Hamilton-Jacobi-Bellman (HJB) equation. Heuristic dynamic …
[PDF][PDF] Applications of the deep galerkin method to solving partial integro-differential and hamilton-jacobi-bellman equations
Abstract We extend the Deep Galerkin Method (DGM) introduced in Sirignano and
Spiliopoulos (2018) to solve a number of partial differential equations (PDEs) that arise in …
Spiliopoulos (2018) to solve a number of partial differential equations (PDEs) that arise in …