This paper reviews the current state of the art on reinforcement learning (RL)-based
feedback control solutions to optimal regulation and tracking of single and multiagent …

Composite adaptation and learning techniques were initially proposed for improving
parameter convergence in adaptive control and have generated considerable research …

This article presents a novel efficient experience-replay-based adaptive dynamic
programming (ADP) for the optimal control problem of a class of nonlinear dynamical …

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 …

This article addresses the problem of distributed path following of multiple under-actuated
autonomous surface vehicles (ASVs) with completely unknown kinetic models. An integrated …

This paper aims to develop a safe learning scheme of the USV interception mission. A safe
Lyapunov boundary deep deterministic policy gradient (SLDDPG) algorithm is presented for …

Reinforcement learning (RL) has been successfully employed as a powerful tool in
designing adaptive optimal controllers. Recently, off-policy learning has emerged to design …

A key assumption in the development of system identification and adaptive control schemes
is the availability of a regression model which is linear in the unknown parameters (of the …

We present some new results on the dynamic regressor extension and mixing parameter
estimators for linear regression models recently proposed in the literature. This technique …

Concurrent learning (CL) is a recently developed adaptive update scheme that can be used
to guarantee parameter convergence without requiring persistent excitation. However, this …