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Optimal and Autonomous Control Using Reinforcement Learning: A Survey

Kiumarsi, Bahare ; Vamvoudakis, Kyriakos G. ; Modares, Hamidreza ; Lewis, Frank L.

IEEE transaction on neural networks and learning systems, 2018-06, Vol.29 (6), p.2042-2062

United States: IEEE

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Título:
Optimal and Autonomous Control Using Reinforcement Learning: A Survey
Autor: Kiumarsi, Bahare ; Vamvoudakis, Kyriakos G. ; Modares, Hamidreza ; Lewis, Frank L.
Assuntos: Algorithm design and analysis ; Algorithms ; Approximation algorithms ; Autonomy ; data-based optimization ; Electronic & video games ; Feedback control ; Games ; H-infinity control ; Heuristic algorithms ; Learning ; Learning (artificial intelligence) ; Machine learning ; Multiagent systems ; Optimal control ; Reinforcement ; reinforcement learning (RL) ; State-of-the-art reviews ; System dynamics
É parte de: IEEE transaction on neural networks and learning systems, 2018-06, Vol.29 (6), p.2042-2062
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: 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 \mathcal {H}_{2} and \mathcal {H}_\infty 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.
Editor: United States: IEEE
Idioma: Inglês

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Optimal and Autonomous Control Using Reinforcement Learning: A Survey

Kiumarsi, Bahare ; Vamvoudakis, Kyriakos G. ; Modares, Hamidreza ; Lewis, Frank L.

United States: IEEE

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