Optimal and Autonomous Control using Reinforcement Learning: A Survey
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.
B. Kiumarsi et al., "Optimal and Autonomous Control using Reinforcement Learning: A Survey," IEEE Transactions on Neural Networks and Learning Systems, pp. 1-21, Institute of Electrical and Electronics Engineers (IEEE), Dec 2017.
The definitive version is available at https://doi.org/10.1109/TNNLS.2017.2773458
Electrical and Computer Engineering
Keywords and Phrases
Approximation Algorithms; Continuous Time Systems; Discrete Time Control Systems; Heuristic Algorithms; Learning Algorithms; Multi Agent Systems; Optimal Control Systems; Optimization; Algorithm Design and Analysis; Autonomy; Data-Based Optimization; Games; Learning (artificial Intelligence); Optimal Controls; System Dynamics; Reinforcement Learning; Optimal Control; Reinforcement Learning (RL)
International Standard Serial Number (ISSN)
Article - Journal
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Dec 2017