Abstract
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 language | English (US) |
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Journal | IEEE Transactions on Mobile Computing |
DOIs | |
State | Accepted/In press - 2021 |
All Science Journal Classification (ASJC) codes
- Software
- Computer Networks and Communications
- Electrical and Electronic Engineering
Keywords
- Companies
- Heuristic algorithms
- Mobile computing
- Nash equilibrium
- Nash equilibrium
- Optimization
- Trajectory
- UAV deployment
- Unmanned aerial vehicles
- decentralized training
- differentiated services
- imitation learning