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

, , , , и . CoRR, (2020)

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Algis Ivanauskas

Johann Ivanauskas

Ivana Mesic

Rolle der N-terminalen Domänen (NTDs) bei der Assemblierung und Funktion von N-Methyl-D-Aspartat (NMDA)-Rezeptoren. TU Darmstadt, (2009)

Ivana Tolic

Ivana Crnovcic

 

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