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Supplementary material to the paper "An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control".

License: GNU General Public License v3.0

R 0.10% HTML 99.90%
agents ai artificial-intelligence building building-energy-optimization control deep-learning deep-reinforcement-learning energy hvac machine-learning reinforcement-learning

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paper-drl_building's Issues

Run period bound

Hi
I am interested about the run period bounds that were employed in the training of your models. Furthermore, may I request a graphical or numerical representation of the ‘Cooling_Setpoint_RL’ and ‘Heating_Setpoint_RL’ from the monitor file of the evaluation conducted on the 5zone env?

I have encountered numerous challenges when utilizing sinergym. It appears that there are several blind spots associated with the usage of sinergym for Deep Reinforcement Learning

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