What you'll learn:
- SOLVE THE TRAVELLING SALESMAN PROBLEM
- Understand and implement Genetic Algorithms
- Get the general AI framework
- Understand how to use this tool to your own projects
- SOLVE A COMPLEX MAZE
- Understand and implement Q-Learning
- Get the right Q-Learning intuition
- Understand how to use this tool to your own projects
- SOLVE MOUNTAIN CAR FROM OPENAI GYM
- Understand and implement Deep Q-Learning
- Build Artificial Neural Networks with Keras
- Use the environments provided in OpenAI Gym
- Understand how to use this tool to your own projects
- SOLVE SNAKE
- Understand and implement Deep Convolutional Q-Learning
- Build Convolutional Neural Networks with Keras
- Understand how to use this tool to your own projects
Ever wish you could harness the power of Deep Learning and Machine Learning to craft intelligent bots built for gaming?
If you’re looking for a creative way to dive into Artificial Intelligence, then ‘Artificial Intelligence for Simple Games’ is your key to building lasting knowledge.
Learn and test your AI knowledge of fundamental DL and ML algorithms using the fun and flexible environment of simple games such as Snake, the Travelling Salesman problem, mazes and more.
1. Whether you’re an absolute beginner or seasoned Machine Learning expert, this course provides a solid foundation of the basic and advanced concepts you need to build AI within a gaming environment and beyond.
2. Key algorithms and concepts covered in this course include: Genetic Algorithms, Q-Learning, Deep Q-Learning with both Artificial Neural Networks and Convolutional Neural Networks.
3. Dive into SuperDataScience’s much-loved, interactive learning environment designed to build knowledge and intuition gradually with practical, yet challenging case studies.
4. Code flexibility means that students will be able to experiment with different game scenarios and easily apply their learning to business problems outside of the gaming industry.
‘AI for Simple Games’ Curriculum
Section #1 — Dive into Genetic Algorithms by applying the famous Travelling Salesman Problem to an intergalactic game. The challenge will be to build a spaceship that travels across all planets in the shortest time possible!
Section #2 — Learn the foundations of the model-free reinforcement learning algorithm, Q-Learning. Develop intuition and visualization skills, and try your hand at building a custom maze and design an AI able to find its way out.
Section #3 — Go deep with Deep Q-Learning. Explore the fantastic world of Neural Networks using the OpenAI Gym development environment and learn how to build AIs for many other simple games!
Section #4 — Finish off the course by building your very own version of the classic game, Snake! Here you’ll utilize Convolutional Neural Networks by building an AI that mimics the same behavior we see when playing Snake.
