LightMARL : smart swarm coordination in urban spaces

Multi-Agent Reinforcement Learning (MARL) systems generally require substantial computational resources and high-bandwidth communication, which restricts their deployment to centralized cloud infrastructures. This thesis introduces LightMARL, an optimization framework that facilitates effective multi-agent coordination on resource-constrained edge devices while ensuring coordination quality and real-time performance. LightMARL tackles three primary challenges: computational efficiency, communication overhead, and scalability. To improve computation, the framework utilizes neural network quantization, structured pruning, and knowledge distillation tailored for multi-agent policy optimization (MAPPO). These methods decrease model size and computational demands while maintaining essential coordination behaviors. To enhance communication efficiency, vector quantization combined with delta compression, attention-driven selective information sharing, and predictive protocols minimize bandwidth usage and latency. The framework is structured as a modular Python system incorporating C++ components, supporting deployment on Nvidia Jetson platforms. Experimental validation in simulated environments, including drone swarms, vehicle platooning, and sensor networks, demonstrates the framework’s effectiveness. This study illustrates that complex multi-agent coordination is possible on edge devices, enabling new applications in autonomous functionality.

Name thesis:degree_name

Master of Science in Computer Sciences

Discipline thesis:degree_discipline

Computer Science

Grantor

The University of Texas at Austin

Year dc:date.issued

2025

Author dc:creator

• Murali, Abhejay

Advisors dc:contributor.advisor

• Jiao, Junfeng
• Zhu, Yuke

• Edge computing
• Multi-Agent Reinforcement Learning
• Drone swarms

Language dc:language.iso

English

Identifier URI

https://doi.org/10.26153/tsw/61831

OAI identifier oai:identifier

oai:repositories.lib.utexas.edu:2152/134508