# Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching

arXiv: https://arxiv.org/abs/2307.13158 · HTML: https://mlsanity.com/paper/2307.13158/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 24 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Action Branching Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1711.08946/) (2017)
- [Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies](https://mlsanity.com/paper/2208.02249/) (2022)
- [Efficient Drone Mobility Support Using Reinforcement Learning](https://mlsanity.com/paper/1911.09715/) (2019)
- [Disconnectivity-Aware Energy-Efficient Cargo-UAV Trajectory Planning with Minimum Handoffs](https://mlsanity.com/paper/2106.14276/) (2021)
- [Optimization of Speed and Network Deployment for Reliable V2I Communication in the Presence of Handoffs and Interference](https://mlsanity.com/paper/2307.06852/) (2023)

## Built on by (most cited first, 5 of 5)

- [Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks](https://mlsanity.com/paper/2405.11331/) (2024)
- [Hierarchical and Collaborative LLM-Based Control for Multi-UAV Motion and Communication in Integrated Terrestrial and Non-Terrestrial Networks](https://mlsanity.com/paper/2506.06532/) (2025)
- [Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning](https://mlsanity.com/paper/2405.18984/) (2024)
- [Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity](https://mlsanity.com/paper/2605.11509/) (2026)
- [Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach](https://mlsanity.com/paper/2607.18604/) (2026)

## Abstract (arXiv)

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.