Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

This paper addresses the optimal control problem known as the linear quadratic regulator in
the case when the dynamics are unknown. We propose a multistage procedure, called …

The adaptive optimal feedback stabilization is investigated in this article for discounted
guaranteed cost control of uncertain nonlinear dynamical systems. Via theoretical analysis …

Reinforcement Learning (RL) and its integration with deep learning have achieved
impressive performance in various robotic control tasks, ranging from motion planning and …

In this work, we have extended the current success of deep learning and reinforcement
learning to process control problems. We have shown that if reward hypothesis functions are …

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal
tracking control problem of unknown nonlinear multiagent systems (MASs). Different from …

This article is concerned with the stability of the closed-loop system using various control
policies generated by value iteration. Some stability properties involving admissibility …

Approximate dynamic programming techniques usually rely on the feedback of the
measurement of the complete state, which is generally not available in practical situations. In …

In this article, a novel value iteration scheme is developed with convergence and stability
discussions. A relaxation factor is introduced to adjust the convergence rate of the value …

In this paper, we propose a model-free solution to the linear quadratic regulation (LQR)
problem of continuous-time systems based on reinforcement learning using dynamic output …