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
Course Features
- Lecture 1
- Quiz 0
- Duration 10 weeks
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
