Dynamic UAV Deployment for Differentiated Services: A Multi-Agent Imitation Learning Based Approach

Xiaojie Wang, Zhaolong Ning, Song Guo, Miaowen Wen, Lei Guo, Vincent Poor

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed -Nash equilibrium. Performance results show that our algorithm has significant superiority on average profits, utilities and execution time compared with other representative algorithms.

Original languageEnglish (US)
JournalIEEE Transactions on Mobile Computing
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


  • Companies
  • Heuristic algorithms
  • Mobile computing
  • Nash equilibrium
  • Nash equilibrium
  • Optimization
  • Trajectory
  • UAV deployment
  • Unmanned aerial vehicles
  • decentralized training
  • differentiated services
  • imitation learning


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