Diffusion Models for Smarter UAVs: Decision-Making and Modeling
Yousef Emami, Hao Zhou, Luis Almeida, Kai Li
cs.LG, cs.AI
Submitted: 2026-08-18
Updated: 2026-08-19
License: http://creativecommons.org/publicdomain/zero/1.0/
The gist: Uncrewed Aerial Vehicles (UAVs) are increasingly used in modern communication networks.
Terminology
Abstract
Uncrewed Aerial Vehicles (UAVs) are increasingly used in modern communication networks. However, challenges in decision-making and digital modeling continue to hinder their rapid development. Reinforcement Learning (RL) algorithms face limitations such as low sample efficiency and limited data versatility, which are further amplified in UAV communications scenarios. Additionally, Digital Twin (DT) modeling presents significant challenges in decision-making and data management. RL models, often integrated into DT frameworks to address these issues, require large amounts of training data to make accurate predictions. Unlike traditional approaches that focus on class boundaries, Diffusion Models (DMs)-a new class of generative AI-learn the underlying probability distribution from training data and can generate reliable new patterns based on this learned distribution. DT and RL have complementary roles in enabling intelligent, data-driven UAV operations. DMs further enhance this synergy by addressing data scarcity, improving modeling accuracy, and generating realistic scenarios, which benefit both DT simulations and RL training. In this paper, we explore the integration of DMs with RL and DT. Simulation results confirm the effectiveness and benefits of DMs in generating neighbor velocity estimates in a four-UAV swarm coordination task using Deep Reinforcement Learning (DRL).
Sources
- Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases
- Diffusion Models for Reinforcement Learning: A Survey
- Generative AI for Advanced UAV Networking
- Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses
- Regularized Conditional Diffusion Model for Multi-Task Preference Alignment
- DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models
- Digital Twins and Testbeds for Supporting AI Research with Autonomous Vehicle Networks
- Diffusion Models as Network Optimizers: Explorations and Analysis
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