This repository contains reinforcement learning projects that showcase different algorithms, including Q-Learning and Deep Q-Network (DQN) implementations. You will find an agent for an autonomous taxi and another for playing space invaders. These projects are designed to demonstrate how intelligent agents learn and improve through experience.
To begin using these projects, follow the steps outlined below. We aim to make it as simple as possible, so please read through each section.
Before you download, check that your system meets these basic requirements:
- Operating System: Windows, macOS, or Linux
- Processor: Dual-core processor or better
- Memory: At least 4 GB of RAM
- Storage Space: At least 100 MB of free space for installation
To download the projects, visit the Releases page:
On this page, you will find the available versions. Click on the version you want to download. It will usually be provided in a ZIP format.
- Find the release you wish to download.
- Click on the link that ends with
.zipto save the file to your computer. - Once downloaded, locate the ZIP file in your downloads folder.
- Extract the contents of the ZIP file to a folder of your choice.
After extracting the files, you can run the projects by following these directions:
- Open the folder where you extracted the files.
- Look for a file named
start.bat(for Windows) orrun.sh(for macOS and Linux). - Double-click on the file to start the program.
The application will launch in a new window. Follow the on-screen instructions to use the intelligent agents.
Here are some of the features you will find in these projects:
- Autonomous Taxi Agent: This agent learns how to navigate a city and pick up passengers efficiently.
- Space Invaders Agent: This agent plays the classic game and improves its score through learning.
- Visual Learning Process: Watch how the agents adapt and improve over time through visual feedback.
These projects include work on various notable topics in reinforcement learning:
- Deep Neural Networks
- Deep Reinforcement Learning
- Q-Learning
- Scratch Implementations of Reinforcement Learning Algorithms
Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with its environment. It learns from the rewards or penalties it receives from its actions.
No, you do not need programming skills. The applications are designed to be user-friendly for non-technical users. Just follow the instructions in this guide.
Yes, if you are interested in learning more, you can explore the code. However, we recommend that only users with a basic understanding of programming do this.
If you have questions or need help, we encourage you to visit the Issues section of the repository. There, you can ask questions and get support from other users and contributors.
Feel free to explore and enjoy learning about reinforcement learning projects. We hope you find these tools useful for understanding intelligent agents!