Technical Report: A Hierarchical Dynamically Weighting Deep Reinforcement Learning Method for Multi-UAV Multi-Task Coordination

TL;DR

This paper presents a hierarchical dynamic weighting deep reinforcement learning framework for multi-UAV multi-task coordination in emergency scenarios, enhancing decision stability and efficiency.

Contribution

It introduces a novel hierarchical DRL method with episode-level and step-level modules for adaptive task balancing in dynamic environments.

Findings

Faster convergence compared to traditional methods

More stable training process

Higher task completion efficiency in simulations

Abstract

This paper investigates the multi-UAV multi-task coordination problem in infrastructure-less emergency scenarios, where UAVs collaboratively are required to jointly perform aerial image acquisition and ground-user communication. To tackle the challenge of balancing heterogeneous tasks within dynamic environments, we propose a hierarchical dynamic weighting Deep Reinforcement Learning (DRL) framework. Specifically, an episode-level module is introduced to capture global task preferences, while a step-level module adaptively adjusts the objective weights according to real-time system conditions. By integrating global and instantaneous weights, the proposed framework improves decision stability and responsiveness during task execution. Simulation results demonstrate that the proposed method achieves faster convergence, more stable training, and higher task completion efficiency than conventional works.