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 systems. Existing RL solutions to both optimal <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.
Paper
Full text
Optimal and Autonomous Control Using Reinforcement Learning: A Survey
Semantic Scholar · Computer Science · 2018
Abstract
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 systems. Existing RL solutions to both optimal <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.