Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914646520233984 |
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| author | Yan, Zijiang Jaafar, Wael Selim, Bassant Tabassum, Hina |
| author_facet | Yan, Zijiang Jaafar, Wael Selim, Bassant Tabassum, Hina |
| contents | This paper presents a deep reinforcement learning solution for optimizing multi-UAV cell-association decisions and their moving velocity on a 3D aerial highway. The objective is to enhance transportation and communication performance, including collision avoidance, connectivity, and handovers. The problem is formulated as a Markov decision process (MDP) with UAVs' states defined by velocities and communication data rates. We propose a neural architecture with a shared decision module and multiple network branches, each dedicated to a specific action dimension in a 2D transportation-communication space. This design efficiently handles the multi-dimensional action space, allowing independence for individual action dimensions. We introduce two models, Branching Dueling Q-Network (BDQ) and Branching Dueling Double Deep Q-Network (Dueling DDQN), to demonstrate the approach. Simulation results show a significant improvement of 18.32% compared to existing benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_13158 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching Yan, Zijiang Jaafar, Wael Selim, Bassant Tabassum, Hina Machine Learning Robotics Systems and Control This paper presents a deep reinforcement learning solution for optimizing multi-UAV cell-association decisions and their moving velocity on a 3D aerial highway. The objective is to enhance transportation and communication performance, including collision avoidance, connectivity, and handovers. The problem is formulated as a Markov decision process (MDP) with UAVs' states defined by velocities and communication data rates. We propose a neural architecture with a shared decision module and multiple network branches, each dedicated to a specific action dimension in a 2D transportation-communication space. This design efficiently handles the multi-dimensional action space, allowing independence for individual action dimensions. We introduce two models, Branching Dueling Q-Network (BDQ) and Branching Dueling Double Deep Q-Network (Dueling DDQN), to demonstrate the approach. Simulation results show a significant improvement of 18.32% compared to existing benchmarks. |
| title | Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching |
| topic | Machine Learning Robotics Systems and Control |
| url | https://arxiv.org/abs/2307.13158 |