Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …

The adaptive dynamic programming (ADP) technology has been widely used benefiting
from its recursive structure in forward and the prospective conception of reinforcement …

The idea of optimization can be regarded as an important basis of many disciplines and
hence is extremely useful for a large number of research fields, particularly for artificial …

In this article, we propose a way to enhance the learning framework for zero-sum games
with dynamics evolving in continuous time. In contrast to the conventional centralized actor …

In this article, through adaptive critic, a dual event-triggered (DET) constrained control
scheme is established for discrete-time nonlinear zero-sum games. The neural networks are …

This article develops an adaptive neural-network (NN) boundary control scheme for a
flexible manipulator subject to input constraints, model uncertainties, and external …

Optimal control problems are ubiquitous in practical engineering applications and social life
with the idea of cost or resource conservation. Based on the critic learning scheme, adaptive …

Due to limited workspace and safety requirements for practical underactuated mechanical
systems, it is necessary to restrict all to-be-controlled variables and their velocities within …

In order to address zero-sum game problems for discrete-time (DT) nonlinear systems, this
article develops a novel event-triggered control (ETC) approach based on the deterministic …

In this article, the data-based two-player zero-sum game problem is considered for linear
discrete-time systems. This problem theoretically depends on solving the discrete-time game …