Self-driving go-kart with Unity-ML

Self-driving go-kart with Unity-ML: Deep learning applied to a self-driving car simulation

🚀 Course Description: Dive into the fascinating world of machine learning where artificial intelligence meets the thrill of racing. This course is your gateway to understanding the intricacies of machine vision and reinforcement learning through the lens of creating a self-driving go-kart in Unity. 🎮⚫️

What You'll Learn:

• Introduction to Machine Learning: Get a solid foundation in ML, focusing on how it can be applied to real-world scenarios like autonomous vehicle control.

• Unity ML Agents: Although the Unity ML-Agents toolkit has evolved (and you should always check the official documentation for updates), the principles of machine learning taught in this course will remain unchanged.

• Machine Vision Techniques: Explore how computers 'see' and interpret the world, a crucial step for any self-driving system.

• Reinforcement Learning: Understand the concepts behind one of the most powerful approaches to training AI in complex environments.

• Deep Neural Networks: Learn how these networks can be structured and trained, with practical examples in Unity.

• Hands-On Practice: Follow step-by-step instructions to implement a PID controller, train a neural network using supervised learning, and develop a neural network through reinforcement learning.

Course Structure:

1. Theoretical Foundations: 📚

   • Understanding the problem domain.
   • The role of machine vision and reinforcement learning in autonomous systems.

2. Practical Implementations: 🛠️

   • Setting up the Unity environment with the provided template.
   • Experimenting with different ML approaches to control the go-kart:
     • PID Controller: A simple yet effective feedback mechanism.
     • Imitation Learning: Training a neural network using predefined data.
     • Reinforcement Learning: Letting an agent learn through trial and error.

3. Performance Optimization: 🎯

   • Analyzing the results and understanding where improvements can be made.
   • Enhancing the models to achieve better performance on the track.

Why Take This Course?

• Interactive Learning: Combine theoretical knowledge with hands-on experience.
• Real-World Application: Learn skills that are highly applicable in today's tech-driven job market.
• Community Engagement: Join a community of learners and contributors who share your passion for AI and gaming.

Prerequisites:

• Basic understanding of Python programming.
• Familiarity with the Unity interface.
• An interest in machine learning and its applications in real-time systems.

Course Materials Provided:

• Unity template for a self-driving go-kart simulation.
• Code files for each of the three AI control methods.
• Step-by-step instructions to get you started on your coding journey.

Ready to embark on this autonomous adventure? Let's accelerate into the world of machine learning and Unity together! 🏁✨

Note: Remember that the Unity ML agents library is rapidly evolving, so some aspects of the code might change. Always refer to the official migration guide for the latest updates and best practices. Happy learning, racers! 🚀🤓