https://github.com/Pulkit-Khandelwal/Reinforcement-Learning-Notebooks

A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python.
https://github.com/Pulkit-Khandelwal/Reinforcement-Learning-Notebooks

Last synced: about 17 hours ago
JSON representation

A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python.

• Host: GitHub
• URL: https://github.com/Pulkit-Khandelwal/Reinforcement-Learning-Notebooks
• Owner: Pulkit-Khandelwal
• Created: 2018-06-02T23:37:52.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-06-21T20:41:50.000Z (almost 6 years ago)
• Last Synced: 2024-03-03T21:39:28.020Z (4 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 9.91 MB
• Stars: 1,047
• Watchers: 62
• Forks: 203
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Lists

• awesome-stars - Reinforcement-Learning-Notebooks - Khandelwal | 1046 | (Jupyter Notebook)
• awesome-stars - Reinforcement-Learning-Notebooks - Khandelwal | 1018 | (Jupyter Notebook)
• awesome-reinforcement-learning - Reinforcement Learning Notebooks
• my-awesome-stars - Pulkit-Khandelwal/Reinforcement-Learning-Notebooks - A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python. (Jupyter Notebook)

README

        
# Reinforcement-Learning-Notebooks

## A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python.

I wrote these notebooks in March 2017 while I took the *COMP 767: Reinforcement Learning* [5] class by Prof. Doina Precup at McGill, Montréal. I highly recommend you to go through the class notes and references of all the papers the intructors have posted on the website.

These notebooks should be used while you read the book and go beyond the same with the referenced papers. I would suggest watching David Silver's videos and reading the book simultaneously. And when you are done with a few chapters, start implementing them. The algorithms follow a pattern and mostly are variants of each other. I have tried my best to explain each notebook's results and possible future directions.

Disclaimer: The code is a little messy. I'd written this when I was not a Pythonista. If you would like to clean them up and want to make it into a nice interface, feel free to contact me. I will be very pleased to collaborate. If you use them then please cite the source and also mention the credits as listed below. Also, email me with ways to improve, let me know if you find any bugs.

Feel free to reach me at [email protected] or see my website [here](https://pulkit-khandelwal.github.io/)

Special Credits:

[1] [Denny Britz](https://github.com/dennybritz/reinforcement-learning)

[2] [Monica Patel](https://monicaopatel.com/)

[3] [Sutton and Barto](https://www.amazon.ca/Reinforcement-Learning-Introduction-Richard-Sutton/dp/0262193981)

[4] [David Silver](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)

[5] [Doina Precup's course](https://www.cs.mcgill.ca/~dprecup/courses/rl.html)