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Deep Reinforcement Learning For Dynamic Spectrum Access

A Python and Jupyter research implementation that trains multiple cognitive-radio users to share scarce channels using multi-agent Deep Q-learning with LSTM-based DRQN, without communication between users.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Deep Reinforcement Learning For Dynamic Spectrum Access — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a code-based research starting point for local training or notebook study of multi-user dynamic spectrum sharing.

Who it is for

  • Researchers exploring multi-agent reinforcement learning for dynamic spectrum access.
  • Learners who want to inspect environment, state-generation, and clustering notebooks.

Documented uses

  • Train a multi-user DQN model for cognitive-radio channel sharing.
  • Explore the environment and state-generation workflow in Jupyter notebooks.
  • Study cluster creation as part of the project implementation.
Before you choose

Requirements & limits

The public GitHub repository is identified as MIT-licensed.

  • The documented model combines multi-agent Deep Q-learning with LSTM cells in a DRQN.
  • The README lists Python, TensorFlow greater than 1.0, NumPy, Matplotlib, and Jupyter as dependencies.
  • The documented workflow uses a terminal training command and Jupyter notebooks.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the documented DQN training command

Clone the repository and start its documented training script from a terminal.

Before you start

  • Python
  • Matplotlib
  • TensorFlow greater than 1.0
  • NumPy
  • Jupyter

Steps

  1. Clone the repository with the command below.

    Documented command
    git clone https://github.com/shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access.git

  2. Change into Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access.

  3. Run the command below in the terminal.

    Documented command
    python train.py

Expected outcome

Starts the repository's DQN training workflow for the multi-user dynamic spectrum access project.

Guide limitations

  • The README does not specify training duration, expected output, or hardware requirements.
Documentation 1 ↗
Source-based practical guide

Open the project notebooks

Launch Jupyter to inspect the notebooks provided for understanding the code.

Before you start

  • Jupyter installed in the local environment.

Steps

  1. Open a terminal in the repository directory.

  2. Run jupyter notebook.

  3. Use the browser to open the notebooks on environment use, state generation, and cluster creation.

Expected outcome

Opens the repository notebooks for step-by-step exploration.

Guide limitations

  • The README names the notebook topics but does not describe their full contents or expected results.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access · Official homepage title · retained in its source language
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