reinforcement-learning
https://github.com/machine-learning-tutorial/reinforcement-learning
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 9 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.0%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: machine-learning-tutorial
- License: gpl-3.0
- Language: Jupyter Notebook
- Default Branch: main
- Size: 3.74 MB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 4
Metadata Files
README.md
Tutorial on basic reinforcement learning concepts
Material for this tutorial
The theoretical lecture can be found here:
The tutorial can be found here:
Citing the tutorial
- This tutorial is registered in Zenodo.
- Please use this DOI when citing this material: https://doi.org/10.5281/zenodo.12649046
bibtex
@software{santamaria_garcia_2024_12649046,
author = {Santamaria Garcia, Andrea},
title = {Tutorial on basic reinforcement learning concepts},
month = jul,
year = 2024,
publisher = {Zenodo},
doi = {10.5281/zenodo.12649046},
url = {https://doi.org/10.5281/zenodo.12649046}
}
Download the repository
Get the repository with Git
You will need to have Git previously installed in your computer. To check if you have it installed, open your terminal and type:
bash
git --version
Git installation in MacOS
bash
brew update
brew install git
Git installation in Linux
In Ubuntu/Debian
bash
sudo apt install git
In CentOS
bash
sudo yum install git
Once you have Git installed open your terminal, go to your desired directory, and type:
bash
git clone https://github.com/machine-learning-tutorial/reinforcement-learning
cd reinforcement-learning
Getting started
You need to install the dependencies before running the notebooks.
Using conda
If you don't have conda installed already and want to use conda for environment management, you can install the miniconda as described here.
- Create a conda env with
conda create -n rl-tutorial python=3.10 - Activate the environment with
conda activate rl-tutorial - Install the required packages via
pip install -r requirements.txt. - Run the following commands:
bash
python -m jupyter contrib nbextension install --user
python -m jupyter nbextension enable varInspector/main
- After the tutorial you can remove your environment with
conda remove -n nn-tutorial --all
Running the tutorial
After installing the package
You can start the jupyter notebook in the terminal, and it will start a browser automatically
bash
python -m jupyter notebook
Alternatively, you can use supported Editor to run the jupyter notebooks, e.g. with VS Code.
Jupyter Notebooks
Use cmd+Enter to execute one cell block
Part 1 Simple Gridworld (no ML libraries)
The first part of the tutorial is in RL_simple_gridworld.ipynb.
Owner
- Name: machine learning tutorial
- Login: machine-learning-tutorial
- Kind: organization
- Repositories: 1
- Profile: https://github.com/machine-learning-tutorial
Citation (CITATION.cff)
cff-version: 1.2.0
title: "Introduction to Reinforcement Learning"
version: 1.8.0
license: "gpl-3.0-or-later"
type: software
abstract: "Introductory lecture and code to learn reinforcement learning from scratch"
message: "If you use this material (notebooks or lecture content) please cite it
as indicated"
authors:
- given-names: Andrea
family-names: Santamaria Garcia
affiliation: University of Liverpool and Cockcroft Institute
orcid: "https://orcid.org/0000-0002-7498-7640"
keywords:
- machine learning
- reinforcement learning
- accelerator physics
GitHub Events
Total
- Release event: 2
- Watch event: 1
- Push event: 1
- Create event: 2
Last Year
- Release event: 2
- Watch event: 1
- Push event: 1
- Create event: 2