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Improved DRL-based energy-efficient UAV control for maximum lifecycle
H Ma, G Yang, X Sun, D Qu, G Chen, X **… - Journal of the Franklin …, 2024 - Elsevier
Unmanned aerial vehicles (UAVs) operating as airborne base stations (UAV-BSs) provide
efficient on-demand services to ground users. UAV-BSs are inherently flexible and mobile …
efficient on-demand services to ground users. UAV-BSs are inherently flexible and mobile …
[HTML][HTML] Energy management of a microgrid considering nonlinear losses in batteries through Deep Reinforcement Learning
D Domínguez-Barbero, J García-González… - Applied Energy, 2024 - Elsevier
The massive deployment of microgrids could play a significant role in achieving
decarbonization of the electric sector amid the ongoing energy transition. The effective …
decarbonization of the electric sector amid the ongoing energy transition. The effective …
A cognitive computing approach for product configuration design based on associative rule mining and deep reinforcement learning
Product configuration design is instrumental in combining available configurable
components to rapidly generate new products that align with customised requirements …
components to rapidly generate new products that align with customised requirements …
A Scalable and Coordinated Energy Management for Electric Vehicles Based on Multiagent Reinforcement Learning Method
R Bian, X Jiang, G Zhao, Y Liu… - … Transactions on Electrical …, 2024 - Wiley Online Library
The electric vehicle (EV) has been popular in recent years, which also brings huge
challenges to the distribution network due to its energy instability. In order to consider the …
challenges to the distribution network due to its energy instability. In order to consider the …
Innovative energy solutions: Evaluating reinforcement learning algorithms for battery storage optimization in residential settings
Z Dou, C Zhang, J Li, D Li, M Wang, L Sun… - Process Safety and …, 2024 - Elsevier
The implementation of BESS (battery energy storage systems) and the efficient optimization
of their scheduling are crucial research challenges in effectively managing the intermittency …
of their scheduling are crucial research challenges in effectively managing the intermittency …
Advances and challenges in learning from experience replay
DE Neves, L Ishitani… - Artificial Intelligence …, 2024 - Springer
From the first theoretical propositions in the 1950s to its application in real-world problems,
Reinforcement Learning (RL) is still a fascinating and complex class of machine learning …
Reinforcement Learning (RL) is still a fascinating and complex class of machine learning …
[HTML][HTML] Enhancing cotton irrigation with distributional actor–critic reinforcement learning
Y Chen, M Lin, Z Yu, W Sun, W Fu, L He - Agricultural Water Management, 2025 - Elsevier
Accurate predictions of irrigation's impact on crop yield are crucial for effective decision-
making. However, current research predominantly focuses on the relationship between …
making. However, current research predominantly focuses on the relationship between …
Traffic Congestion Control with Emergency Awareness and Optimized Communication Infrastructure using Reinforcement Learning and Non-Dominated Sorting …
Urban centers are grappling with increasing traffic congestion, which hampers mobility and
reduces the effectiveness of emergency responses. Current traffic management systems …
reduces the effectiveness of emergency responses. Current traffic management systems …
Dynamic equivalent modelling for active distributed network considering adjustable loads charging characteristics
As more renewable energy generators and adjustable loads such as electric vehicles are
being connected to the power grids, load modelling of the distribution network becomes …
being connected to the power grids, load modelling of the distribution network becomes …