Reinforcement Learning: A Comprehensive Open-Source Course

Python Jupyter Notebook MATLAB Submitted 14 August 2023Published 28 March 2026
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Editor: @ethanwhite (all papers)
Reviewers: @sconde (all reviews), @arokem (all reviews)

Authors

Ali Hassan Ali Abdelwanis (0009-0001-5853-5900), Barnabas Haucke-Korber (0000-0003-0862-2069), Darius Jakobeit (0009-0002-1576-2465), Wilhelm Kirchgässner (0000-0001-9490-1843), Marvin Meyer (0009-0008-2879-7118), Maximilian Schenke (0000-0001-5427-9527), Hendrik Vater (0009-0005-0654-8741), Oliver Wallscheid (0000-0001-9362-8777), Daniel Weber (0000-0003-3367-5998)

Citation

Abdelwanis et al., (2026). Reinforcement Learning: A Comprehensive Open-Source Course. Journal of Open Source Education, 9(97), 306, https://doi.org/10.21105/jose.00306

@article{Abdelwanis2026, doi = {10.21105/jose.00306}, url = {https://doi.org/10.21105/jose.00306}, year = {2026}, publisher = {The Open Journal}, volume = {9}, number = {97}, pages = {306}, author = {Ali Abdelwanis and Barnabas Haucke-Korber and Darius Jakobeit and Wilhelm Kirchgässner and Marvin Meyer and Maximilian Schenke and Hendrik Vater and Oliver Wallscheid and Daniel Weber}, title = {Reinforcement Learning: A Comprehensive Open-Source Course}, journal = {Journal of Open Source Education} }
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data science TensorFlow PyTorch Jupyter notebook reproducible workflow open science reinforcement learning exploratory data analysis machine learning supervised learning

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ISSN 2577-3569