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 (4 months ago)

Commits 10 (last one 4 months ago)

Stargazers 21 (0 this week)

Watchers 1 (0 this week)

Forks 2

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RepositoryStats indexes 595,856 repositories, of these AliAmini93/WSN-Scheduling-with-Reinforcement-Learning is ranked #594,414 (0th percentile) for total stargazers, and #544,643 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #17,506/17,543.

AliAmini93/WSN-Scheduling-with-Reinforcement-Learning is also tagged with popular topics, for these it's ranked: reinforcement-learning (#1,336/1337)

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: 2024-11-25 @ 04:25am, id: 842174958 / R_kgDOMjKR7g