arjun7579/maddpg-drone-coverage
Implementation of a multi-agent UAV swarm system using MADDPG. Drones learn to avoid collisions, maximize user coverage, and coordinate efficiently through advanced multi-agent reinforcement learning.
This project helps operations engineers and drone fleet managers design and simulate drone swarms that efficiently cover areas while avoiding collisions. It takes in parameters for a simulated 2D environment, including the number of drones, users to cover, and obstacles. The output is a trained drone swarm capable of coordinated movement to maximize coverage and minimize collisions, along with optional real-time visualizations of their behavior.
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Use this if you need to research or develop control strategies for multiple drones to work together autonomously to survey an area or provide network coverage.
Not ideal if you are looking for a ready-to-deploy solution for physical drones or a tool for single-drone path planning.
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Python
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Last pushed
Jun 27, 2025
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