rl4aa24-tutorial
Hands-on tutorial about Meta RL and GP-MPC at the RL4AA'24 workshop.
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 5 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (9.1%) to scientific vocabulary
Repository
Hands-on tutorial about Meta RL and GP-MPC at the RL4AA'24 workshop.
Basic Info
- Host: GitHub
- Owner: RL4AA
- License: gpl-3.0
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://rl4aa.github.io/rl4aa24-tutorial
- Size: 10.1 MB
Statistics
- Stars: 13
- Watchers: 3
- Forks: 1
- Open Issues: 1
- Releases: 3
Metadata Files
README.md
Tutorial on Meta-Reinforcement Learning and GP-MPC at the RL4AA'24 Workshop
This repository contains the material for the second day of the RL4AA'24 event.
Homepage for RL4AA Collaboration: https://rl4aa.github.io/
Theory slides for the tutorial
- Concepts to overcome challenges in applying RL to accelerators, Simon Hirländer
Python tutorial: meta reinforcement learning implementation example
- GitHub repository containing the material: https://github.com/RL4AA/RL4AA24
- Tutorial in slide form: here
Getting started
- First, download the material to your local disk by cloning the repository:
git clone https://github.com/RL4AA/rl4aa24-tutorial.git - If you don't have git installed, you can click on the green button that says "Code", and choose to download it as a
.zipfile.
Part 1 Meta-RL
The material is located in the meta-rl folder.
bash
cd meta-rl
Part 2 Model-based RL
The material is located in the gp-mpc folder.
bash
cd gp-mpc
Citing the tutorial
This tutorial is uploaded to Zenodo. Please use the following DOI when citing this code:
bibtex
@software{hirlaender_2024_10887397,
title = {{Tutorial on Meta-Reinforcement Learning and GP-MPC at the RL4AA'24 Workshop}},
author = {Hirlaender, Simon and Kaiser, Jan and Xu, Chenran and Santamaria Garcia, Andrea},
year = 2024,
month = mar,
publisher = {Zenodo},
doi = {10.5281/zenodo.10887397},
url = {https://doi.org/10.5281/zenodo.10887397},
version = {v1.0.2}
}
Owner
- Name: RL4AA
- Login: RL4AA
- Kind: organization
- Repositories: 1
- Profile: https://github.com/RL4AA
Citation (CITATION.cff)
abstract: '<p>Update the title of the repository: "Tutorial on Meta-Reinforcement Learning and GP-MPC at the RL4AA''24 Workshop"</p>' authors: - affiliation: PLUS University Salzburg family-names: Hirlaender given-names: Simon orcid: 0000-0002-2634-3437 - affiliation: Deutsches Elektronen-Synchrotron DESY family-names: Kaiser given-names: Jan orcid: 0000-0003-3445-0678 - affiliation: Karlsruhe Institute of Technology family-names: Xu given-names: Chenran orcid: 0000-0002-5034-2207 - affiliation: Karlsruhe Institute of Technology family-names: Santamaria Garcia given-names: Andrea orcid: 0000-0002-7498-7640 cff-version: 1.2.0 date-released: '2024-03-27' doi: 10.5281/zenodo.10887397 license: - cc-by-4.0 repository-code: https://github.com/RL4AA/rl4aa24-tutorial/tree/v1.0.2 title: Tutorial on Meta-Reinforcement Learning and GP-MPC at the RL4AA'24 Workshop type: software version: v1.0.2
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Dependencies
- actions/checkout v2 composite
- actions/setup-python v2 composite
- s0/git-publish-subdir-action develop composite
- cpymad ==1.15.0
- gpytorch ==1.11
- gymnasium ==0.29.1
- matplotlib ==3.8.2
- notebook ==7.0.7
- numpy ==1.26.3
- pandas ==2.2.0
- pyyaml ==6.0.1
- scipy ==1.12.0
- seaborn ==0.13.2
- stable-baselines3 ==2.2.1
- tensorboard ==2.15.1
- torch ==2.1.2
- tqdm ==4.66.1
- cpymad *
- gpytorch *
- gymnasium >=0.29
- matplotlib *
- notebook *
- numpy *
- pandas *
- pyyaml *
- scipy *
- seaborn *
- stable_baselines3 *
- tensorboard *
- torch >=2.1.0
- tqdm *
- actions/checkout v3 composite
- actions/setup-python v3 composite
- py-actions/flake8 v2 composite