AliAmini93 / WSN-Scheduling-with-Reinforcement-Learning

Developed a reinforcement learning framework using Deep Q-Networks (DQN) to optimize scheduling in Wireless Sensor Networks (WSN), enhancing energy efficiency and state estimation through a custom simulation environment.

Date Created 2024-08-13 (3 months ago)

Commits 10 (last one 3 months ago)

Stargazers 20 (0 this week)

Watchers 1 (0 this week)

Forks 2

License mit

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RepositoryStats indexes 584,353 repositories, of these AliAmini93/WSN-Scheduling-with-Reinforcement-Learning is ranked #583,097 (0th percentile) for total stargazers, and #535,930 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #17,065/17,098.

AliAmini93/WSN-Scheduling-with-Reinforcement-Learning is also tagged with popular topics, for these it's ranked: reinforcement-learning (#1,309/1309)

All Topics

deep-qnetworks stable-baselines3 energy-optimization scheduling-algorithms reinforcement-learning wireless-sensor-networks

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10 commits on the default branch (main) since jan '22

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updated: 2024-11-10 @ 08:35pm, id: 842174958 / R_kgDOMjKR7g