Optimal and Autonomous Control Using Reinforcement Learning: A Survey | IEEE Journals & Magazine | IEEE Xplore

Optimal and Autonomous Control Using Reinforcement Learning: A Survey


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 multiagen...Show More

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 H2 and H 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.
Page(s): 2042 - 2062
Date of Publication: 07 December 2017

ISSN Information:

PubMed ID: 29771662

Funding Agency:


References

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