Reinforcement learning

4 total

14JUL 2026
Akademy
From Optimal Control to Reinforcement Learning: A Motivating Example from the Perspective of Linear Quadratic Regulators
From Optimal Control to Reinforcement Learning: A Motivating Example from the Perspective of Linear Quadratic Regulators Fredson Silva De Souza Aguiar, Visiting Master Student. University of Sorbonne Paris Nord Repository with code and trained models available at FAU DCN-AvH GitHub: https://github.com/DCN-FAU-AvH/oc_rl_lqr We motivate the study of interconnections and relevant results at the interface between Control […]From Optimal Control to Reinforcement Learning: A Motivating Example from the Perspective of Linear Quadratic Regulators Fredson Silva De Souza Aguiar, Visiting Master Student. University of Sorbonne Paris Nord Repository with code and trained models available at FAU DCN-AvH GitHub: https://github.com/DCN-FAU-AvH/oc_rl_lqr We motivate the study of interconnections and relevant results at the interface between Control […]
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01DEC 2023
Hub
PINNs Introductory Code for the Heat Equation
PINNs Introductory Code for the Heat Equation This repository provides some basic insights on Physics Informed Neural Networks (PINNs) and their implementation. PINNs are numerical methods based on the universal approximation capacity of neural networks, aiming to approximate solutions of partial differential equations. Recently, extensive focus has been on approximating solutions of various equations, leading […]PINNs Introductory Code for the Heat Equation This repository provides some basic insights on Physics Informed Neural Networks (PINNs) and their implementation. PINNs are numerical methods based on the universal approximation capacity of neural networks, aiming to approximate solutions of partial differential equations. Recently, extensive focus has been on approximating solutions of various equations, leading […]
Read more about PINNs Introductory Code for the Heat Equation
12JUL 2023
Hub
The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach
The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach Motivation This post shows the source code from the paper “The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach”. (See reference below) We study the combination of the alternating direction method of multipliers (ADMM) with physics-informed neural networks (PINNs) for […]The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach Motivation This post shows the source code from the paper “The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach”. (See reference below) We study the combination of the alternating direction method of multipliers (ADMM) with physics-informed neural networks (PINNs) for […]
Read more about The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach
03JUL 2023
Hub
Reinforcement learning as a new perspective into controlling physical systems
Reinforcement learning as a new perspective into controlling physical systems Introduction Optimal control addresses the problem of bringing a system from an initial state to a target state, like a satellite that we want to send into orbit using the least possible amount of fuel. Since the last century, mathematics has helped develop powerful numerical […]
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