# Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control arXiv: https://arxiv.org/abs/2210.00945 ยท HTML: https://mlsanity.com/paper/2210.00945/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 90 references that are in the map. - First posted on arXiv 3 Oct 2022. ## Builds on (most cited first, 30 of 32) - [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013) - [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017) - [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017) - [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016) - [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016) - [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019) - [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018) - [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022) - [Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications](https://mlsanity.com/paper/2008.02608/) (2020) - [Multi-agent Reinforcement Learning for Networked System Control](https://mlsanity.com/paper/2004.01339/) (2020) - [BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification](https://mlsanity.com/paper/2104.14783/) (2021) - [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016) - [Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond](https://mlsanity.com/paper/1903.05289/) (2019) - [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control](https://mlsanity.com/paper/2201.05843/) (2022) - [Survey of Important Issues in UAV Communication Networks](https://mlsanity.com/paper/1603.08462/) (2016) - [Millimeter Wave Cellular Wireless Networks: Potentials and Challenges](https://mlsanity.com/paper/1401.2560/) (2014) - [Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks](https://mlsanity.com/paper/2112.02543/) (2021) - [Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networks](https://mlsanity.com/paper/2001.03611/) (2020) - [Edge-enabled V2X Service Placement for Intelligent Transportation Systems](https://mlsanity.com/paper/2001.06288/) (2020) - [A Vision of C-V2X: Technologies, Field Testing and Challenges with Chinese Development](https://mlsanity.com/paper/2002.08736/) (2020) - [Artificial Intelligence for UAV-enabled Wireless Networks: A Survey](https://mlsanity.com/paper/2009.11522/) (2020) - [The Role of Caching in Future Communication Systems and Networks](https://mlsanity.com/paper/1805.11721/) (2018) - [Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks](https://mlsanity.com/paper/1902.10932/) (2019) - [Wireless Video Caching and Dynamic Streaming under Differentiated Quality Requirements](https://mlsanity.com/paper/1806.01594/) (2018) - [Survey on Aerial Radio Access Networks: Toward a Comprehensive 6G Access Infrastructure](https://mlsanity.com/paper/2102.07087/) (2021) - [A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking](https://mlsanity.com/paper/2104.09204/) (2021) - [Backhaul-aware Robust 3D Drone Placement in 5G+ Wireless Networks](https://mlsanity.com/paper/1702.08395/) (2017) - [On-Demand Density-Aware UAV Base Station 3D Placement for Arbitrarily Distributed Users with Guaranteed Data Rates](https://mlsanity.com/paper/1904.10881/) (2019) - [Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2106.14203/) (2021) - [The New Frontier in RAN Heterogeneity: Multi-tier Drone-Cells](https://mlsanity.com/paper/1604.00381/) (2016) ## Built on by (most cited first, 5 of 5) - [Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems](https://mlsanity.com/paper/2302.04445/) (2023) - [Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility](https://mlsanity.com/paper/2211.06890/) (2022) - [Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning](https://mlsanity.com/paper/2211.15375/) (2022) - [Navigating the Smog: A Cooperative Multi-Agent RL for Accurate Air Pollution Mapping through Data Assimilation](https://mlsanity.com/paper/2407.12539/) (2024) - [Maximizing User Connectivity in AI-Enabled Multi-UAV Networks: A Distributed Strategy Generalized to Arbitrary User Distributions](https://mlsanity.com/paper/2411.05205/) (2024) ## Abstract (arXiv) This paper addresses a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration (i.e., UAVs work as mobile base stations). The primary objective of the proposed algorithm is to establish dependable mobile access networks for cellular vehicle-to-everything (C-V2X) communication, thereby facilitating the realization of high-quality intelligent transportation systems (ITS). The reliable mobile access services can be achieved in following two ways, i.e., i) energy-efficient UAV operation and ii) reliable wireless communication services. For energy-efficient UAV operation, the reward of our proposed MADRL algorithm contains the features for UAV energy consumption models in order to realize efficient operations. Furthermore, for reliable wireless communication services, the quality of service (QoS) requirements of individual users are considered as a part of rewards and 60GHz mmWave radio is used for mobile access. This paper considers the 60GHz mmWave access for utilizing the benefits of i) ultra-wide-bandwidth for multi-Gbps high-speed communications and ii) high-directional communications for spatial reuse that is obviously good for densely deployed users. Lastly, the comprehensive and data-intensive performance evaluation of the proposed MADRL-based algorithm for multi-UAV positioning is conducted in this paper. The results of these evaluations demonstrate that the proposed algorithm outperforms other existing algorithms.