In this paper, we develop an online synchronous approximate optimal learning algorithm
based on policy iteration to solve a multiplayer nonzero-sum game without the requirement …

This article proposes new inverse reinforcement learning (RL) algorithms to solve our
defined Adversarial Apprentice Games for nonlinear learner and expert systems. The games …

H∞ control is a powerful method to solve the disturbance attenuation problems that occur in
some control systems. The design of such controllers relies on solving the zero-sum game …

We consider a multi-player differential game, referred to as a reach-avoid game, in which
one set of attacking players attempts to reach a target while avoiding both obstacles and …

This paper presents a concurrent learning-based actor-critic-identifier architecture to obtain
an approximate feedback-Nash equilibrium solution to an infinite horizon N-player nonzero …

An approximate online equilibrium solution is developed for an N-player nonzero-sum game
subject to continuous-time nonlinear unknown dynamics and an infinite horizon quadratic …

This paper seeks to combine differential game theory with the actor-critic-identifier
architecture to determine forward-in-time, approximate optimal controllers for formation …

This paper is concerned with the approximate solution of Hamilton–Jacobi–Isaacs (HJI)
equation for constrained-input nonlinear continuous-time systems with unknown dynamics …

In this paper, the nearly optimal solution for discrete-time (DT) affine nonlinear control
systems in the presence of partially unknown internal system dynamics and disturbances is …

In this paper, we develop an adaptive control algorithm for addressing security for a class of
networked vehicles that comprise a formation of human‐driven vehicles sharing kinematic …