🌊 ❄️ ⛄ 🕳️ 🎯 RL-Frozen-Lake 🎯 🕳️ ⛄ ❄️ 🌊

This repository contains a reinforcement learning agent designed to solve the Frozen Lake problem. The agent uses Q-learning algorithm to learn the optimal policy for navigating a grid of frozen lake tiles, while avoiding holes and reaching the goal. The implementation is in Python and uses the OpenAI Gym environment. To run the code, you will need to install the following dependencies of python.

For more information regarding environment :- https://gymnasium.farama.org/environments/toy_text/frozen_lake/

Library	Version	Description
Python 🐍	3.11.2	For development of RL mini project (.ipynb)
Numpy 🏃	1.23.5	For fast numeric / linear algebra computation
Gym 🏋️	0.24.0	For using open AI gym environment of Frozen_Lake_v1
Pygame 🎮	2.3.0	For rendering open AI gym environment of Frozen_Lake_v1

File Structure layout

RL                              # Project Directory
├── frozenLake_qTable.pkl       # Trained Q-learning Model
├── Mini_Project.ipynb          # Python Code File
├── Q-Formula.png               # Formula of Q-Learning
├── README.md                   # Markdown file of the project
└── requirements.txt            # Dependencies of the project

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• Want to contribute ?

  1. Folk the repository in github.

  2. Create a clone in your local system using below command :-

     git clone [url_repo]

  3. In cmd type :-

     cd [project]

  4. Then create a dedicated branch for the work with effective name using below command :-

     git checkout -b [Effective_branch-name]

  5. Then you can work on the respective repository in your local machine. After you did your changes then follow below commands :-

     git add [file_names]

     git commit -m "commit message"

     git push origin [branch_you_created]

  6. Open pull request in github with dedicated comment.

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