AlgoRL
A repository of the most common RL algorithms and examples of their applications.
Here, I'd like to gather the python code equivalents of the most well known RL algorithms.
The aim is not to create the most efficient algorithms but to be the most clear for learners.
Showing all steps and hard-to-understand points.
Help, in the form of reviews, contributions, bug-reporting is greatly encouraged and appreciated.
Sources:
- Primary source is the pseudo-code reported in Reinforcement Learning: An Introduction, 2nd edition by Richard S. Sutton and Andrew G. Barto. MIT Press, Cambridge, MA, 2018.
- Grokking Deep Reinforcement Learning, by Miguel Morales. 2020
Table of contents
Installation
Clone the project
git clone https://www.github.com/MattiaCinelli/algorlRun Locally
cd algorl
pip install .Examples
Development and Contribute
I welcome contributors (of all experience levels) to improve this package and expand its scope and reach.
If you have never worked on open source project before or you want to brush up your memory here, check out these links:
Please do not hesitate to contact me to report issues or new ideas.