This monograph presents a new framework for learning-based control synthesis of
continuous-time dynamical systems with unknown dynamics. The new design paradigm …

In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed
to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear …

In this article, we consider an iterative adaptive dynamic programming (ADP) algorithm
within the Hamiltonian-driven framework to solve the Hamilton–Jacobi–Bellman (HJB) …

This paper is concerned with a new discrete-time policy iteration adaptive dynamic
programming (ADP) method for solving the infinite horizon optimal control problem of …

In this paper, an integral reinforcement learning (IRL) algorithm on an actor–critic structure is
developed to learn online the solution to the Hamilton–Jacobi–Bellman equation for partially …

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time
optimal control solution for unknown constrained-input systems. The proposed PI algorithm …

This exposition discusses continuous-time reinforcement learning (CT-RL) for the control of
affine nonlinear systems. We review four seminal methods that are the centerpieces of the …

In this article, we introduce some recent research trends within the field of
adaptive/approximate dynamic programming (ADP), including the variations on the structure …

The Hamilton–Jacobi–Bellman (HJB) equation corresponding to constrained control is
formulated using a suitable nonquadratic functional. It is shown that the constrained optimal …

In this paper, the near-optimal control problem for a class of nonlinear discrete-time systems
with control constraints is solved by iterative adaptive dynamic programming algorithm. First …