Framework for learning handover algorithms using deep reinforcement learning.
CODE IS CURRENTLY BEING REVISED. A NEW VERSION WILL BE AVAILABLE SOON.
HandoverOptimDRL is a framework designed to facilitate the development and evaluation of handover algorithms using deep reinforcement learning, i.e., proximal policy optimization (PPO). It provides tools and environments to simulate the 3GPP handover protocol and to train and evaluate a PPO-based handover protocol.
This repository contains the source code, data sets and trained PPO model for the paper A Deep Reinforcement Learning-based Approach for Adaptive Handover Protocols [1].
To install the HandoverOptimDRL package, follow these steps:
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Clone the repository:
git clone https://github.com/kit-cel/HandoverOptimDRL
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Navigate to the project directory:
cd HandoverOptimDRL -
Set up the environment:
python setup.py install
You are now ready to use the HandoverOptimDRL framework for your projects.
[1] Johannes Voigt, Peter J. Gu, and Peter M. Rost, "A Deep Reinforcement Learning-based Approach for Adaptive Handover Protocols". Available: ""