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Aurora: Reinforcement Learning Resources for Performance-Oriented Congestion Control

Aurora is a reinforcement learning-based project focused on optimizing congestion control for network performance. This repository provides resources for training and evaluating models in this domain.

Training Instructions

1. Navigate to the Training Directory

Open a terminal and navigate to the /gym/ directory within the project:

cd /gym/

2. Install Required Dependencies

Conda Environment Setup

To set up the Conda environment for this project, follow these steps:

  1. Ensure Conda is Installed

    If Conda is not installed, download and install it from the official Conda website.

  2. Create the Conda Environment

    Use the provided environment.yml file to create the environment by running the following command:

    conda env create -f environment.yml
conda activate aurora

3. Run the Training Script

Once the dependencies are installed, initiate the training by running the train.py script. You can specify the model type (small, mid, big, or all) to train different versions of the Aurora model. Use the following command:

python train.py --model {small, mid, big, all}
  • small: Trains the small version of the model.
  • mid: Trains the mid-sized model.
  • big: Trains the large version of the model.
  • all: Trains all three model versions sequentially.

Once training is complete, you will see the following message:

[Done] Finished training {model_type} model

4. Testing

Testing is not provided. Refer to the original paper for the necessary settings.