This project demonstrates the concept of Multi-Armed Bandits using the Greedy Policy. Multi-Armed Bandits is a classic problem in the field of reinforcement learning and decision-making. The project is implemented as a Python notebook (.ipynb), allowing you to easily run and experiment with the code.

The objective of this project is to explore and understand the Greedy Policy in the context of the Multi-Armed Bandits problem. The Multi-Armed Bandits problem involves a gambler (an agent) facing multiple slot machines (bandits), each with different reward probabilities. The agent's goal is to maximize its total reward over a certain number of trials.

The project repository contains the following files:

• Multi_Armed_Bandits_using_Greedy_Policy.ipynb: This is the main Python notebook that contains the implementation of the Multi-Armed Bandits problem using the Greedy Policy. You can run this notebook locally or on platforms like Jupyter Notebook or Google Colab.

• README.md: The README file provides an overview of the project and instructions for running and contributing to it.

To get started with the Multi-Armed-Bandits-using-Greedy-Policy project, follow these steps:

1. Clone the Repository: Start by cloning this repository to your local machine using the following command:

   git clone https://github.com/salimhammadi15/Multi-Armed-Bandits-using--Greedy-Policy.git
   

2. Open the Jupyter Notebook: Launch Jupyter Notebook or any other compatible environment and navigate to the cloned repository.

3. Run the Notebook: Open the Multi_Armed_Bandits_using_Greedy_Policy.ipynb notebook and run the cells sequentially. The notebook provides detailed explanations and code comments to guide you through the implementation.

4. Experiment and Explore: Once the notebook is running, feel free to experiment with different parameters, reward distributions, and exploration-exploitation trade-offs. Observe how the agent's performance changes and learn more about the behavior of the Greedy Policy in Multi-Armed Bandits.

Contributions are welcome to enhance the Multi-Armed-Bandits-using-Greedy-Policy project. To contribute, follow these steps:

1. Fork the Repository: Click on the "Fork" button at the top right corner of the repository page. This will create a copy of the repository in your GitHub account.

2. Make Changes: Create a new branch, make your desired changes in the notebook, and add any additional features or improvements.

3. Test Your Changes: Run the notebook and ensure that your modifications work correctly.

4. Commit and Push: Commit your changes and push them to your forked repository:

   git add .
   git commit -m "Your commit message"
   git push origin your-branch-name
   

5. Create a Pull Request: Go to the original repository on GitHub and click on the "New Pull Request" button. Fill out the details and submit the pull request. Your changes will be reviewed by the project maintainers.

This project is licensed under the MIT License. See the LICENSE file for more information.

Special thanks to the developers and researchers in the field of reinforcement learning for their contributions and inspiration in the implementation of the Multi-Armed Bandits problem using the Greedy Policy.