Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/arindal1/blackjack-qlearning-rl
Python script for implementing Q-learning to train an agent to play the game of Blackjack.
https://github.com/arindal1/blackjack-qlearning-rl
blackjack game machine-learning qlearning
Last synced: 8 days ago
JSON representation
Python script for implementing Q-learning to train an agent to play the game of Blackjack.
- Host: GitHub
- URL: https://github.com/arindal1/blackjack-qlearning-rl
- Owner: arindal1
- Created: 2023-10-15T16:29:42.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-16T03:24:56.000Z (about 1 year ago)
- Last Synced: 2023-10-17T16:13:45.273Z (about 1 year ago)
- Topics: blackjack, game, machine-learning, qlearning
- Language: Python
- Homepage:
- Size: 33.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AI Blackjack w/ Q-learning
This project implements a Q-learning-based reinforcement learning solution to play the popular casino card game, Blackjack. The implementation consists of two main components: `BlackjackQLearning`, representing the Q-learning agent, and `BlackjackGame`, modeling the game of Blackjack.
## Introduction
The Q-learning agent (`BlackjackQLearning`) employs Q-tables to learn an optimal strategy for playing Blackjack. It uses an ε-greedy strategy for action selection during training and updates the Q-table based on rewards obtained during gameplay. The Blackjack game (`BlackjackGame`) simulates the actions of the player and dealer, allowing the Q-learning agent to learn and improve its strategy through interactions.
## Project Structure
The project structure is as follows:
- `qlearning.py`: Contains the Q-learning agent implementation.
- `blackjack.py`: Contains the Blackjack game implementation.
- `main.py`: Entry point for training the Q-learning agent and evaluating its performance.
- `ABOUT_THE_CODE.md`: Markdown that explains the code.
## How to Run
To run the project and train the Q-learning agent, follow these steps:
1. Ensure you have Python installed on your machine.
2. Clone this repository:
```bash
git clone https://github.com/yourusername/blackjack-q-learning.git
cd blackjack-q-learning
```
3. Run the training script:
```bash
python main.py
```
4. The agent will be trained for a specified number of episodes, and test games will be played to evaluate its performance.
***Note:*** You can refer to [ABOUT_THE_CODE](ABOUT_THE_CODE.md) for a line-by-line breakdown.
## Configuration and Parameters
You can modify the training parameters and other configurations by editing the constants and variables in `main.py` and the relevant parts of `qlearning.py`.
- `alpha`: Learning rate for the Q-learning algorithm.
- `gamma`: Discount factor for future rewards.
- `epsilon`: Exploration probability for the ε-greedy strategy.
- `episodes`: Number of training episodes for the Q-learning agent.
- `test_games`: Number of test games to evaluate the agent's performance.
## Results
After training and testing, the project will display the win rate of the Q-learning agent in playing Blackjack.
## Contributing
Feel free to contribute to this project by opening issues or creating pull requests. Contributions are welcome and encouraged!
## Contact
If you have any questions or suggestions related to this project, you can reach out to me at:
- GitHub: [arindal1](https://github.com/arindal1)
- LinkedIn: [arindalchar](https://www.linkedin.com/arindalchar/)
- Twitter: [arindal_17](https://twitter.com/arindal_17)
---
## Keep creating 🚀