| Trajectory Optimization for Autonomous Flying Base Station via Reinforcement Learning H Bayerlein, P de Kerret, D Gesbert IEEE International Workshop on Signal Processing Advances in Wireless …, 2018 | 136 | 2018 |
| Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning H Bayerlein, M Theile, M Caccamo, D Gesbert IEEE Open Journal of the Communications Society, 2021 | 131 | 2021 |
| UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning M Theile, H Bayerlein, R Nai, D Gesbert, M Caccamo IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020, 2020 | 111 | 2020 |
| UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach H Bayerlein, M Theile, M Caccamo, D Gesbert IEEE Global Communications Conference (GLOBECOM) 2020, 2020 | 73 | 2020 |
| UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning M Theile, H Bayerlein, R Nai, D Gesbert, M Caccamo 20th International Conference on Advanced Robotics (ICAR) 2021, 2020 | 55 | 2020 |
| An Experiment about Estimating the Number of Instruments in Polyphonic Music: A Comparison Between Internet and Laboratory Results. M Schoeffler, FR Stöter, H Bayerlein, B Edler, J Herre 14th International Society for Music Information Retrieval Conference (ISMIR …, 2013 | 34 | 2013 |
| Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots H Bayerlein, R Gangula, D Gesbert 52nd Asilomar Conference on Signals, Systems and Computers 2018, 2018 | 18 | 2018 |
| Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance R Trumpp, H Bayerlein, D Gesbert IEEE Intelligent Vehicles Symposium (IV) 2022, 2022 | 16 | 2022 |
| Comparison of a 2D-and 3D-based Graphical User Interface for Localization Listening Tests M Schoeffler, S Westphal, A Adami, H Bayerlein, J Herre Proceedings of the EAA Joint Symposium on Auralization and Ambisonics 2014, 2014 | 16 | 2014 |
| Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks O Esrafilian, H Bayerlein, D Gesbert IEEE Global Communications Conference (GLOBECOM) 2021, 2021 | 7 | 2021 |
| Learning to recharge: UAV coverage path planning through deep reinforcement learning M Theile, H Bayerlein, M Caccamo, AL Sangiovanni-Vincentelli arXiv preprint arXiv:2309.03157, 2023 | 3 | 2023 |
| Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks J Chen, O Esrafilian, H Bayerlein, D Gesbert, M Caccamo IEEE Global Communications Conference Workshops (GLOBECOM) 2023, 2023 | 1 | 2023 |
| Machine Learning Methods for UAV-aided Wireless Networks H Bayerlein Sorbonne Université, 2021 | 1 | 2021 |
| Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance R Trumpp, H Bayerlein, D Gesbert BMW-EURECOM-TUM 8th Summer School on Frontiers in Machine Intelligence, 2022 | | 2022 |
| Optimal Trajectory of Autonomous Flying Base Stations via Reinforcement Learning H Bayerlein, P de Kerret, D Gesbert 1st TUM-EURECOM Workshop on Communications and Security, 2017 | | 2017 |
| ER-Force Team Description Paper for RoboCup 2014 H Bayerlein, A Danzer, M Eischer, A Hauck, M Hoffmann, P Kallwies, ... RoboCup 2014, 2014 | | 2014 |
David GesbertProfessor of Electrical Engineering, Dean and Director at EURECOM在 eurecom.fr 的电子邮件经过验证
