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Parallel Transfer Learning in Multi-Agent Systems: What, when and how to transfer?

, , , and . IJCNN, page 1-8. IEEE, (2019)

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Multi-agent Deep Reinforcement Learning for Zero Energy Communities., and . ISGT Europe, page 1-5. IEEE, (2019)An RL-based Approach to Improve Communication Performance and Energy Utilization in Fog-based IoT., , and . WiMob, page 324-329. IEEE, (2019)Learning run-time compositions of interacting adaptations., and . SEAMS@ICSE, page 108-114. ACM, (2020)Parallel Transfer Learning in Multi-Agent Systems: What, when and how to transfer?, , , and . IJCNN, page 1-8. IEEE, (2019)Adaptive Height Optimization for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach., , , , and . IEEE Access, (2023)A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT., , and . CoRR, (2021)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)Enabling Deep Reinforcement Learning on Energy Constrained Devices at the Edge of the Network., and . WCNC, page 2547-2552. IEEE, (2022)