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Titel:

Recurrent Soft Actor Critic Reinforcement Learning for Demand Response Problems

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Ludolfinger, Ulrich; Zinsmeister, Daniel; Perić, Vedran S.; Hamacher, Thomas; Hauke, Sascha; Martens, Maren
Stichworte:
Training; Deep learning; Uncertainty; Buildings; Reinforcement learning; Markov processes; Demand response; Machine Learning; Reinforcement Learning; Partially Observable Markov Decision Process; Recurrent Soft Actor Critic; Home Energy Management; Demand Response
Kongress- / Buchtitel:
2023 IEEE Belgrade PowerTech
Jahr:
2023
Seiten:
1-6
Volltext / DOI:
doi:10.1109/PowerTech55446.2023.10202844
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