Q-Learning Implementation

This repository contains a simple implementation of the Q-learning algorithm from scratch. Q-learning is a model-free reinforcement learning algorithm used to find the optimal action-selection policy for a given problem. The algorithm learns to select actions by learning the quality of actions in a given state.

Requirements

• Python 3.x
• NumPy

Installation

1. Clone the repository:

git clone https://github.com/Arker123/Q-learning.git

2. Change directory to the project folder:

cd Q-learning

3. Install the required dependencies:

pip install -r requirements.txt

Usage

You can use the Q-learning algorithm by running the main.py script:

python main.py

The script contains an example environment and a Q-learning agent that learns to navigate through the environment. You can modify the environment and the parameters of the Q-learning algorithm to test different scenarios.

Contributing

Contributions are always welcome! If you have any suggestions or improvements, please feel free to create an issue or a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• The implementation is inspired by the classic work of Watkins and Dayan.
• Thanks to the open-source community for providing valuable resources and examples.