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.
- Python 3.x
- NumPy
- Clone the repository:
git clone https://github.com/Arker123/Q-learning.git- Change directory to the project folder:
cd Q-learning- Install the required dependencies:
pip install -r requirements.txt
You can use the Q-learning algorithm by running the main.py script:
python main.pyThe 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.
Contributions are always welcome! If you have any suggestions or improvements, please feel free to create an issue or a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
- The implementation is inspired by the classic work of Watkins and Dayan.
- Thanks to the open-source community for providing valuable resources and examples.