reinforcement-learning

My first steps at reinforcement learning with a snake game

Getting Started

These instructions will give you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Requirements for the software and other tools to build, test and push

• Git
• Docker
• Visual Studio Code
• Dev Containers Extension for Visual Studio Code

If you have a nvidia GPU make sure the CUDA Toolkit is installed for utilizing your GPU.

This repo is build as a VS Code Dev Container, meaning inside of Visual Studio Code a Docker Container is started.
All installation and development will be made inside this container.
With this abstraction layer, a standardized environment for everyone is created.

Make sure to set the MinIO Alias in the .devcontainer/.env in the following format:

MC_HOST_origin=https://USERNAME:PASSWORD@s3.lukas-gysin.ch

📌 Dataset selection
Make sure the correct dataset is selected.
By default each ml project has its own dataset.
If you want to use a shared dataset, edit the DATASET variable in the Makefile.
This step must only be configred once, directly after clonig this template

📌 MinIO Bucket
If you are not working with an existing dataset, make sure the MinIO bucket exists.

mc mb origin/dataset-reinforcement-learning

After cloning the repository update pip, install the python requirements and configure the git filter.

make requirements
git config filter.strip-notebook-output.clean 'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --ClearMetadataPreprocessor.enabled=True --to=notebook --stdin --stdout --log-level=ERROR'

Project Organization

├── LICENSE            <- MIT Open-source license
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
│
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         src and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── src                <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes `src` a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models          
    │   └── train.py            <- Code to train models
    │
    └── plots.py                <- Code to create visualizations

Authors

• Lukas Gysin - Main Contributor & Project Owner

License

This project is licensed under the MIT License

Acknowledgments

• Billie Thompson - Provided README Template
• Contributor Covenant - Provided CODE_OF_CONDUCT Template