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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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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| 2026-07-24 | 255 |
| 2026-07-25 | 255 |
| 2026-07-28 | 255 |
| 2026-07-30 | 255 |
| 2026-07-31 | 256 |
| 2026-08-06 | 256 |
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# Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access ## Dependencies 1. python [link](https://www.python.org) 2. matplotlib [link](https://matplotlib.org/) 3. tensorflow > 1.0 [link](https://www.tensorflow.org/) 4. numpy [link](https://www.numpy.org/) 5. jupyter [link](http://jupyter.org/) We recommend to install with [Anaconda](https://anaconda.org/anaconda/python) ### To train the DQN ,run on terminal ```bash git clone https://github.com/shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access.git cd Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access python train.py ``` To understand the code , I have provided jupyter notebooks: 1. How to use environment.ipynb 2. How to generate states.ipynb 3. How_to_create_cluster.ipynb To run notebook,run on terminal ```bash jupyter notebook ``` Default browser will open ipynb files. Run each command one by one This work is an inspiration from the paper ``` O. Naparstek and K. Cohen, “Deep multi-user reinforcement learning for dynamic spectrum access in multichannel wireless networks,” to appear in Proc. of the IEEE Global Communications Conference (GLOBECOM), Dec. 2017 ```
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:03de8e1084c9ffcc, topic:reinforcement-learning, name:reinforcement learning, readme:reinforcement learning
matched fp:03de8e1084c9ffcc, topic:tensorflow