Deep Reinforcement Learning Driven UAV-assisted Edge Computing

Liang Zhang, Bijan Jabbari, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review


Unmanned aerial vehicles (UAVs) are playing a critical role in provisioning instant connectivity and computational needs of Internet of Things devices (IoTDs), especially in crisis and disaster management. In this work, we focus on optimizing trajectories of UAVs along which IoTDs are served with communication and computing resources in multiple time slots. The quality of experience (QoE) of an IoTD depends on its latency performance; we thus aim to maximize the average aggregate QoE of all IoTDs over all time slots. However, this is a non-convex, nonlinear and mixed discrete optimization problem, which is difficult to solve and obtain the optimal solution. We thus propose two deep reinforcement learning algorithms to solve this problem by considering UAV path planning, user assignment, bandwidth and computing resource assignment. We compare the performance of our proposed algorithms through simulations with three baseline cases: 1) with fixed UAV locations, 2) without UAVs, and 3) the fixed UAV trajectories. We demonstrate that the deep reinforcement learning algorithms perform better than all baseline cases.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Internet of Things Journal
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Autonomous aerial vehicles
  • computation offloading
  • Delays
  • edge computing
  • Internet of Things
  • joint optimization
  • Machine learning
  • path planning
  • Quality of experience
  • Reinforcement learning
  • Task analysis
  • Trajectory
  • unmanned aerial vehicle (UAV)


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