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REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association.

, , , , and . IEEE Trans. Veh. Technol., 71 (1): 5-20 (2022)

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Adaptive Height Optimization for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach., , , , and . IEEE Access, (2023)Parallel Transfer Learning in Multi-Agent Systems: What, when and how to transfer?, , , and . IJCNN, page 1-8. IEEE, (2019)An RL-based Approach to Improve Communication Performance and Energy Utilization in Fog-based IoT., , and . WiMob, page 324-329. IEEE, (2019)Multi-agent Deep Reinforcement Learning for Zero Energy Communities., and . ISGT Europe, page 1-5. IEEE, (2019)Learning run-time compositions of interacting adaptations., and . SEAMS@ICSE, page 108-114. ACM, (2020)Causal Counterfactuals for Improving the Robustness of Reinforcement Learning., , and . CoRR, (2022)Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points., , and . CoRR, (2021)Auto-COP: Adaptation generation in Context-oriented Programming using Reinforcement Learning options., and . Inf. Softw. Technol., (December 2023)A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT., , and . CoRR, (2021)Deep W-Networks: Solving Multi-Objective Optimisation Problems with Deep Reinforcement Learning., , and . ICAART (2), page 17-26. SCITEPRESS, (2023)