Residual Physics and Post-Posed Shielding for Safe Deep Reinforcement Learning Method

Deep reinforcement learning (DRL) has been researched for computer room air conditioning unit control problems in data centers (DCs). However, two main issues limit the deployment of DRL in actual systems. First, a large amount of data is needed. Next, as a mission-critical system, safe control need...

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Veröffentlicht in:	IEEE transactions on cybernetics 2024-02, Vol.54 (2), p.865-876
Hauptverfasser:	Zhang, Qingang, Mahbod, Muhammad Haiqal Bin, Chng, Chin-Boon, Lee, Poh-Seng, Chui, Chee-Kong
Format:	Artikel
Sprache:	eng
Schlagworte:	Air conditioning Algorithms Atmospheric modeling Constraint modelling Control methods Cooling Data center (DC) cooling Data centers Deep learning Deep reinforcement learning Energy conservation law Physics Prediction models Predictive models residual physics safe deep reinforcement learning (DRL) Safety Shielding Temperature distribution Thermodynamics
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