Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access

Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access

shkrwnd

Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication

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253 Stars
97 Forks
253 Watchers
Jupyter Notebook Language
mit License
100 SrcLog Score
Cost to Build
$5.3K
Market Value
$11.2K
How is this calculated?

Growth over time

9 data points  ·  2021-07-01 → 2026-04-01
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What is the shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access GitHub project? Description: "Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication". Written in Jupyter Notebook. Explain what it does, its main use cases, key features, and who would benefit from using it.

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