# MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks

arXiv: https://arxiv.org/abs/2608.01128 · HTML: https://mlsanity.com/paper/2608.01128/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 65 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 17 of 17)

- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2109.11251/) (2021)
- [Jump-Start Reinforcement Learning](https://mlsanity.com/paper/2204.02372/) (2022)
- [Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks](https://mlsanity.com/paper/1801.01803/) (2018)
- [Age of Information: Whittle Index for Scheduling Stochastic Arrivals](https://mlsanity.com/paper/1801.03422/) (2018)
- [EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring Using Atrous Convolutional Feature Fusion](https://mlsanity.com/paper/2104.14006/) (2021)
- [AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2105.04196/) (2021)
- [Soft Action Priors: Towards Robust Policy Transfer](https://mlsanity.com/paper/2209.09882/) (2022)
- [Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile Communications](https://mlsanity.com/paper/2303.16737/) (2023)
- [UAV-Aided Lifelong Learning for AoI and Energy Optimization in Non-Stationary IoT Networks](https://mlsanity.com/paper/2312.00334/) (2023)
- [Ensuring Data Freshness for Blockchain-enabled Monitoring Networks](https://mlsanity.com/paper/2011.07012/) (2020)
- [Trajectory and Power Optimization for Multi-UAV Enabled Emergency Wireless Communications Networks](https://mlsanity.com/paper/2407.11896/) (2024)
- [The Rise of UAV Fleet Technologies for Emergency Wireless Communications in Harsh Environments](https://mlsanity.com/paper/2407.17044/) (2024)
- [Resource Allocation for 5G-UAV Based Emergency Wireless Communications](https://mlsanity.com/paper/2407.17094/) (2024)
- [Sustainable Task Offloading in Secure UAV-assisted Smart Farm Networks: A Multi-Agent DRL with Action Mask Approach](https://mlsanity.com/paper/2407.19657/) (2024)
- [Efficient Domain Generalization in Wireless Networks with Scarce Multi-Modal Data](https://mlsanity.com/paper/2510.04359/) (2025)

## Built on by (most cited first, 2 of 2)

- [Embodied Semantic Communication for Collective Autonomous Agents: A Tutorial on Representation, Wireless Delivery, and Closed-Loop Coordination](https://mlsanity.com/paper/2609.35936/) (2026)
- [Diffusion-Based Stress Testing of Overload Monitoring for Resilient Emergency Cellular Networks Using Internet CDR Proxies](https://mlsanity.com/paper/2610.04526/) (2026)

## Abstract (arXiv)

In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks. For time-critical rescue missions, information freshness is crucial because decisions based on outdated data may lead to ineffective control actions. This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services. We model bursty packet arrivals using a Markov-modulated Poisson process and adopt finite blocklength theory to capture the coupling among transmission duration, packet completion, and AoI evolution. To balance delay-tolerant long-packet transmission and urgent short-packet response, we propose a mini-slot-embedded scheduling mechanism with adaptive checkpoint-interval selection. We formulate the joint optimization of UAV trajectory control, user scheduling, and checkpoint-interval selection as a multi-agent decision problem, and develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework. MA-HEAD-Net incorporates communication-domain rule priors into a gated multi-head policy, where adaptive gates regulate the contributions of rule-prior and learned-policy logits for different subtasks. The policy and gating components are jointly optimized under multi-agent proximal policy optimization. Simulation results show that MA-HEAD-Net improves policy-formation efficiency compared with representative multi-agent deep reinforcement learning baselines and achieves lower AoI than both learning-based and heuristic methods in dynamic UAV-assisted emergency communication scenarios.