In recent years, researchers have explored the application of Reinforcement Learning (RL)
and Artificial Neural Networks (ANNs) to the control of complex nonlinear and time varying …

The present article enables reinforcement learning (RL)‐based controllers for process
control applications. Existing instances of RL‐based solutions have significant challenges …

In recent years, reinforcement learning (RL) has attracted significant attention from both
industry and academia due to its success in solving some complex problems. This paper …

Artificial Neural Networks (ANN) have been used for a wide variety of chemical applications
because of their ability to learn system features. This paper presents the use of feedforward …

This paper provides a brief introduction to Reinforcement Learning (RL) technology,
summarizes recent developments in this area, and discusses their potential implications for …

In this paper, a new control technique for nonlinear control based on hybrid neural modeling
is proposed. For neural network training, a variant of the well-known gradient steepest …

This paper provides an introduction to Reinforcement Learning (RL) technology,
summarizes recent developments in this area, and discusses their potential implications for …

Reinforcement learning (RL) is a data‐driven approach to synthesizing an optimal control
policy. A barrier to wide implementation of RL‐based controllers is its data‐hungry nature …

Abstract The Hamilton–Jacobi–Bellman (HJB) equation can be solved to obtain optimal
closed-loop control policies for general nonlinear systems. As it is seldom possible to solve …

We propose a new reinforcement learning approach for nonlinear optimal control where the
value function is updated as restricted to control Lyapunov function (CLF) and the policy is …