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Algorithms to solve the DSSE environment, focusing on optimizing drone swarm search and navigation for critical applications.

Home Page: https://pfeinsper.github.io/drone-swarm-search/

License: MIT License

Python 4.94% Jupyter Notebook 95.06%
deep-learning deep-reinforcement-learning rl ai greedy-algorithm multi-agent-reinforcement-learning multiagent-reinforcement-learning python3 pytorch

drone-swarm-search-algorithms's Introduction

PyPI Release 🚀 License: MIT PettingZoo version dependency GitHub stars

DSSE Icon Algorithms for Drone Swarm Search (DSSE)

Welcome to the official GitHub repository for the Drone Swarm Search (DSSE) algorithms. These algorithms are specifically tailored for reinforcement learning environments aimed at optimizing drone swarm coordination and search efficiency.

Explore a diverse range of implementations that leverage the latest advancements in machine learning to solve complex coordination tasks in dynamic and unpredictable environments.

📚 Documentation Links

  • Documentation Site: Access detailed tutorials, usage examples, and comprehensive technical documentation. This resource is essential for understanding the DSSE framework and integrating these algorithms into your projects effectively.

  • DSSE Training Environment Repository: Visit the repository for the DSSE training environment, where you can access the core environment setups and configurations used for developing and testing the algorithms.

  • PyPI Repository: Download the latest release of DSSE, view the version history, and find installation instructions. Keep up with the latest updates and improvements to the algorithms.

🆘 Support and Community

Run into a snag? Have a suggestion? Join our community on GitHub! Submit your queries, report bugs, or contribute to discussions by visiting our issues page. Your input helps us improve and evolve.

drone-swarm-search-algorithms's People

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drone-swarm-search-algorithms's Issues

Requisitos H3

Tentativas:

  • Modificação de probabilidade por onde drones passam;
  • LSTM nas posições do drone + rede centralizada.

Resultados para H1

We will test whether RL outperforms a greedy strategy in scenarios where the PIW moves out of the region of highest probability. This will be evaluated using a grid environment (20x20) with four drones controlled by both centralized and independent Deep Q-Network (DQN) algorithms against a greedy approach. The performance of each method will be assessed under conditions of small dispersion with a probability of detection (POD) set to 1.

Resultados para H2

This hypothesis examines if agents trained with independent neural networks converge faster than when trained with shared networks. Using a similar grid environment, we will deploy four drones to find a PIW under conditions of both small and large dispersion, analyzing the rate of convergence in these scenarios.

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