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Titel:

Off-Policy Risk-Sensitive Reinforcement Learning-Based Constrained Robust Optimal Control

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Li, Cong; Liu, Qingchen; Zhou, Zhehua; Buss, Martin; Liu, Fangzhou
Stichworte:
Convergence; Optimization; Optimal control; Process control; Robustness; Robust control; Periodic structures; Adaptive dynamic programming (ADP); input saturation; off-policy risk-sensitive reinforcement learning (RL); robust control; state constraint
Zeitschriftentitel:
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Jahr:
2023
Band / Volume:
53
Heft / Issue:
4
Seitenangaben Beitrag:
2478-2491
Volltext / DOI:
doi:10.1109/TSMC.2022.3213750
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