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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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