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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

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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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