Generative AI for Smarter UAVs: Enhancing Reinforcement Learning Decision-Making
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 45
Special Issue Editors
Interests: security and privacy; machine learning; AI
Interests: malware analysis; intrusion detection systems; adversarial machine learning; applied machine learning; network security; federated and split learning; differential privacy
Special Issue Information
Dear Colleagues,
Unmanned Aerial Vehicles (UAVs) are increasingly vital to modern communication networks, yet challenges in decision-making hinder their full potential. Reinforcement Learning (RL) offers promising solutions but faces limitations such as low sample efficiency and data scarcity, particularly in dynamic UAV environments. Diffusion Models (DMs), a class of generative AI, present a transformative approach by learning underlying data distributions and generating realistic synthetic data. This capability addresses RL’s data scarcity and improves policy networks, enabling more effective decision-making in UAV communications.
The integration of DMs with RL enhances UAV adaptability and real-time performance. DMs generate diverse synthetic data, improve policy networks, and create realistic simulation environments, overcoming RL's limitations.
This Special Issue invites original research on integrating DMs with RL in UAV communications, focusing on innovative decision-making approaches.
Dr. Yousef Emami
Dr. Rahim Taheri
Guest Editors
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Keywords
- unmanned aerial vehicles (UAVs)
- diffusion models (DMs)
- reinforcement learning (RL)
- policy networks
- synthetic data generation
- data scarcity
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