This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment. So these are the topics:

•  Markov Decision Processes
•  value-iteration and policy-iteration
• Q-learning fundamentals
• pathfinding algorithms with Q-learning
• Q-learning with neural networks

Who this course is for:

• Anyone who wants to understand artificial intelligence and reinforcement learning!

Requirements

• Basics AI knowledge: neural networks in the main