A simple NashQ-learning implementation in Python
Project description
LearningNashQLearning
This is an educational project to see the inner workings of the Nash-Q Learning algorithm. The Nash-Q Learning algorithm is a multi-agent reinforcement learning algorithm that is designed to learn Nash equilibria in general-sum stochastic games. This project is designed to be educational and is not intended to be used in production environments.
What's in here?
You can find the following subpackages in this project:
- Model: Contains the implementation of the Nash-Q Learning algorithm as well as a wide range of classes to define Stochastic Games.
- View: Contains the implementation of a wide variety of widgets to visualize the Nash-Q Learning algorithm. They are designed to be used in python Notebooks via the ipythonwidgets library.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
LearningNashQLearning-0.12.tar.gz
(19.8 kB
view hashes)
Built Distribution
Close
Hashes for LearningNashQLearning-0.12.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | c37f6fb87454fbce0098fe4b0a733e49c38379e4a8d74264b7e788ad222f3273 |
|
MD5 | 6dbac60df07993d3946caa58c51d437e |
|
BLAKE2b-256 | ce3029dd38e2e72df0eebea4d5b749e9f329301b61bc7aba5be98a1d5c1a88ff |
Close
Hashes for LearningNashQLearning-0.12-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 33572e29c644ffc459099d97a885a12ff76c023ef3fdb50806f256aaedaaeb89 |
|
MD5 | bb17fe11f4fb925be2811dcf65a3f414 |
|
BLAKE2b-256 | 78c379c2f56cf5d3b7f672deafc4f873f025abc29bd2b855674767cca9be8c7d |