# Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2504.13554 · HTML: https://mlsanity.com/paper/2504.13554/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services](https://mlsanity.com/paper/2303.13052/) (2023)
- [DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach](https://mlsanity.com/paper/2406.06986/) (2024)
- [Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue](https://mlsanity.com/paper/2309.16709/) (2023)
- [Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities](https://mlsanity.com/paper/2402.18062/) (2024)
- [Generative AI for Advanced UAV Networking](https://mlsanity.com/paper/2404.10556/) (2024)
- [Generative AI for Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2311.06523/) (2023)
- [DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2411.08299/) (2024)
- [Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model](https://mlsanity.com/paper/2405.01555/) (2024)
- [Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning](https://mlsanity.com/paper/2501.06410/) (2025)
- [GainNet: Coordinates the Odd Couple of Generative AI and 6G Networks](https://mlsanity.com/paper/2401.02662/) (2024)
- [Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services](https://mlsanity.com/paper/2411.01458/) (2024)
- [Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices](https://mlsanity.com/paper/2501.15305/) (2025)

## Built on by (most cited first, 3 of 3)

- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin](https://mlsanity.com/paper/2604.07687/) (2026)
- [From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks](https://mlsanity.com/paper/2605.25531/) (2026)

## Abstract (arXiv)

The integration of emerging uncrewed aerial vehicles (UAVs) with artificial intelligence (AI) and ground-embedded robots (GERs) has transformed emergency rescue operations in unknown environments. However, the high computational demands often exceed a single UAV's capacity, making it difficult to continuously provide stable high-level services. To address this, this paper proposes a cooperation framework involving UAVs, GERs, and airships. The framework enables resource pooling through UAV-to-GER (U2G) and UAV-to-airship (U2A) links, offering computing services for offloaded tasks. Specifically, we formulate the multi-objective problem of task assignment and exploration as a dynamic long-term optimization problem aiming to minimize task completion time and energy use while ensuring stability. Using Lyapunov optimization, we transform it into a per-slot deterministic problem and propose HG-MADDPG, which combines the Hungarian algorithm with a GDM-based multi-agent deep deterministic policy gradient. Simulations demonstrate significant improvements in offloading efficiency, latency, and system stability over baselines.