TY - JOUR
T1 - Resilient reinforcement learning and robust output regulation under denial-of-service attacks
AU - Gao, Weinan
AU - Deng, Chao
AU - Jiang, Yi
AU - Jiang, Zhong Ping
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/8
Y1 - 2022/8
N2 - In this paper, we have proposed a novel resilient reinforcement learning approach for solving robust optimal output regulation problems of a class of partially linear systems under both dynamic uncertainties and denial-of-service attacks. Fundamentally different from existing works on reinforcement learning, the proposed approach rigorously analyzes both the resilience of closed-loop systems against attacks and the robustness against dynamic uncertainties. Moreover, we have proposed an original successive approximation approach, named hybrid iteration, to learn the robust optimal control policy, that converges faster than value iteration, and is independent of an initial admissible controller. Simulation results demonstrate the efficacy of the proposed approach.
AB - In this paper, we have proposed a novel resilient reinforcement learning approach for solving robust optimal output regulation problems of a class of partially linear systems under both dynamic uncertainties and denial-of-service attacks. Fundamentally different from existing works on reinforcement learning, the proposed approach rigorously analyzes both the resilience of closed-loop systems against attacks and the robustness against dynamic uncertainties. Moreover, we have proposed an original successive approximation approach, named hybrid iteration, to learn the robust optimal control policy, that converges faster than value iteration, and is independent of an initial admissible controller. Simulation results demonstrate the efficacy of the proposed approach.
KW - Denial-of-service attacks
KW - Hybrid iteration
KW - Reinforcement learning
KW - Robust output regulation
UR - http://www.scopus.com/inward/record.url?scp=85129963203&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129963203&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2022.110366
DO - 10.1016/j.automatica.2022.110366
M3 - Article
AN - SCOPUS:85129963203
SN - 0005-1098
VL - 142
JO - Automatica
JF - Automatica
M1 - 110366
ER -