Date of Award
5-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair/Advisor
Abolfazl Razi
Committee Member
Long Cheng
Committee Member
Rahul Amin
Abstract
Mission planning for a fleet of cooperative autonomous drones in applications that involve serving distributed target points, such as disaster response, environmental monitoring, and surveil- lance, is challenging, especially under partial observability, limited communication range, and uncertain environments. Traditional path-planning algorithms struggle in these scenarios, particu- larly when prior information is not available. To address these challenges, I propose an innovative framework that integrates Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), and transformer-based mechanisms for enhanced multi-agent coordination and collective task ex- ecution. My approach leverages GNNs to model agent-agent and agent-goal interactions through adaptive graph construction, enabling efficient information aggregation and decision-making un- der constrained communication. A transformer-based message-passing mechanism, augmented with edge-feature-enhanced attention, captures complex interaction patterns, while a Double Deep Q-Network (Double DQN) with prioritized experience replay optimizes agent policies in partially observable environments. This integration is carefully designed to address specific requirements of multi-agent navigation, such as scalability, adaptability, and efficient task execution. Experi- mental results demonstrate superior performance, with 90% service provisioning and 100% grid coverage (node discovery), while reducing the average steps per episode to 200, compared to 600 for benchmark methods such as particle swarm optimization (PSO), greedy algorithms and DQN. My framework establishes a robust solution for cooperative Unmanned Aerial Vehicle (UAV) path planning in challenging operational scenarios.
Recommended Citation
Elrod, Michael S., "Graph Based Deep Reinforcement Learning Aided by Transformers for Multi-Agent Cooperation" (2025). All Theses. 4519.
https://open.clemson.edu/all_theses/4519