Multi-Agent Reinforcement Learning for UAV Swarm Coordination

Overview

This project explores multi-agent reinforcement learning (MARL) techniques for coordinating UAV swarms in complex environments. The system enables multiple unmanned aerial vehicles to learn cooperative behaviors through decentralized decision-making while maintaining formation and avoiding collisions.

Key Features

• Decentralized MARL framework for scalable swarm coordination
• Collision avoidance using learned policies
• Formation control with flexible geometric configurations
• Communication-efficient coordination protocols
• Sim-to-real transfer capabilities

Technical Approach

The system employs a decentralized multi-agent deep reinforcement learning architecture where each UAV learns its own policy while considering the actions and states of neighboring agents. The approach uses proximal policy optimization (PPO) with attention mechanisms to handle variable numbers of nearby agents and dynamic communication topologies.

Applications

This work enables coordinated UAV operations for various real-world applications:

• Search and rescue missions with multiple UAVs
• Aerial surveillance and monitoring
• Package delivery in urban environments
• Agricultural surveying and crop monitoring