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Energy management of buildings with energy storage and solar photovoltaic: A diversity in experience approach for deep reinforcement learning agents
Download2024-01-01
Deep reinforcement learning (DRL) is a suitable approach to handle uncertainty in managing the energy consumption of buildings with energy storage systems. Conventionally, DRL agents are trained by randomly selecting samples from a data set, which can result in overexposure to some data...
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Fall 2022
Some real-world deployments of deep reinforcement learning (RL) may require a human-in-the-loop. Whether to ask-for-help, obtain new demonstrations and data, or handle out-of-distribution states, many methods rely on uncertainty estimates from a neural network to determine when to solicit a...