Diffusion Models for Smarter UAVs: Decision-Making and Modeling

arXiv:2501.05819v2 Announce Type: replace 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).

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).

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2501.05819 [cs.LG]

(or arXiv:2501.05819v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2501.05819 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

aiqbench analysis

Diffusion Models for Smarter UAVs: Decision-Making and Modeling

Related coverage