Optimal and Autonomous Control Using Reinforcement Learning: A Survey

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 6; pp. 2042 - 2062
• Main Authors: Kiumarsi, Bahare, Vamvoudakis, Kyriakos G., Modares, Hamidreza, Lewis, Frank L.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Approximation algorithms; Autonomy; Computer & video games; data-based optimization; 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

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...