# shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access

<https://repo.masters-masters.com/en/repos/shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access>

[Home](https://repo.masters-masters.com/en) › [shkrwnd](https://repo.masters-masters.com/en/owners/shkrwnd) › [Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access](https://repo.masters-masters.com/en/repos/shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access)

shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access has 257 stars on GitHub. Its RepoMasters card: Alpha tier. Using multi-agent Deep Q Learning…

| Field | Value |
| --- | --- |
| Owner | [shkrwnd](https://repo.masters-masters.com/en/owners/shkrwnd) |
| Stars | 257 |
| Forks | 96 |
| Language | [Jupyter Notebook](https://repo.masters-masters.com/en/languages/jupyter-notebook) |
| License | MIT |
| Created | 2018 |
| Topics | reinforcement-learning, networks, dqn, tensorflow, multi-agent-system, distributed |
| Tier | [Alpha (250 to 699 ★)](https://repo.masters-masters.com/en/ranks/b_movie) |
| View on GitHub | [shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access](https://github.com/shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access) |

## Repository

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

The tier for repos with 250 to 699 ★. Published odds per card pulled: 20%. Rarity: Uncommon.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.