Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access
Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access copied to clipboard
Published
20 hours ago •
shkrwnd
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
Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access
Dependencies
We recommend to install with Anaconda
To train the DQN ,run on terminal
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:
- How to use environment.ipynb
- How to generate states.ipynb
- How_to_create_cluster.ipynb
To run notebook,run on terminal
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
shkrwnd