In this work, we develop a data-driven approach that leverages the deep reinforcement learning (DRL) technique, to intelligently learn the effective strategy ...
Jun 18, 2017 ， In this work, we develop a data-driven ap- proach that leverages the deep reinforcement learning (DRL) technique, to intelligently learn the ...
In this work, we develop a data-driven approach that leverages the deep reinforcement learning (DRL) technique, to intelligently learn the effective strategy ...
BOPTEST [23] is a standardized virtual building simulator includes multiple building models that can be used to test different RL algorithms and to develop new ...
May 1, 2024 ， This research aims to develop and propose a strategic control learning framework for HVAC systems using the deep reinforcement learning (DRL) approach.
The Artificial Intelligence (AI) development described herein uses model-free Deep Reinforcement Learning (DRL) to minimize energy cost during residential ...
Jan 11, 2024 ， This paper provides a critical and reproducible evaluation, in terms of comfort and energy consumption, of several state-of-the-art DRL algorithms for HVAC ...
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Deep reinforcement learning (DRL) has shown immense potential for high-performing control in a variety of simulated settings, but has not been widely deployed ...
A data-driven approach that leverages the deep reinforcement learning (DRL) technique, to intelligently learn the effective strategy for operating the ...
Oct 26, 2021 ， We present a multi-agent, distributed deep reinforcement learning (DRL) framework based on Energy Plus simulation environment for optimizing HVAC in commercial ...
