This study introduces a mixed-integer programming model designed to determine optimal machine operating states for diverse workloads.
Apr 17, 2024 · This study introduces a mixed-integer programming model designed to determine optimal machine operating states for diverse workloads.
Oct 22, 2024 · The proposed method used novel functions to update the position, velocity, and its main parameters based on the cost function and the defined ...
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What is deep Q-learning strategy?
How does the neural network make the Q-learning algorithm more efficient?
In this paper, we propose a model-free solution, leveraging. Deep Q-Learning to schedule the charging and discharging activities of EVs within a microgrid to.
An improved deep Q-learning algorithm for a trade-off between energy consumption and productivity in batch scheduling. Computers & Industrial Engineering ...
... Improved Ant Colony Algorithm ... An improved deep Q-learning algorithm for a trade-off between energy consumption and productivity in batch scheduling.
In this paper, we propose a model-free solution, leveraging deep Q-learning to schedule the charging and discharging activities of EVs within a microgrid.
Missing: productivity | Show results with:productivity
An improved deep Q-learning algorithm for a trade-off between energy consumption and productivity in batch scheduling. Xu Zheng, Zhen Chen. https://doi.org ...
An improved deep Q-learning algorithm for a trade-off between energy consumption and productivity in batch scheduling. Xu Zheng, Zhen Chen. https://doi.org ...
The deep Q-learning algorithm employs a deep neural network to approximate values. It generally works by feeding the initial state into the neural network.