Title: Reinforcement Learning

Location: US

Company: RWTH Aachen University

About this course:
Unlock the Power of Reinforcement Learning with our MOOC!
Have you ever wondered how machines can learn through trial and error, like a child mastering a new game? Or how computers are able to beat humans in chess? This is where Reinforcement Learning (RL) comes in; a powerful field of artificial intelligence focused on how machines learn by interacting with their environment and receiving feedback.
This MOOC is your gateway to understanding and applying RL. The course starts with building a solid mathematical foundation of the core concepts of RL in a simplified setting to make them rigorous and foster understanding. Building on these fundamentals, we present selected algorithms from modern deep RL, providing you with the basis to study new methods from RL research and put them into practice. The course is accompanied by exercises including programming examples to deepen the understanding of the discussed materials.
Join us to unlock the potential of learning through interaction and take your first steps into the exciting world of Reinforcement Learning!

The basics of Markov decision processes and dynamic programming

The mathematical foundations of tabular reinforcement learning including Monte Carlo and temporal-difference methods

The fundamentals of reinforcement learning with function approximations such as linear models or deep neural networks

Insights into influential modern deep reinforcement learning algorithms

Implementing reinforcement learning algorithms using Python

This MOOC teaches the basics in reinforcement learning. It provides participants with a solid mathematical basis to study new methods from reinforcement learning research and put them into practice.

Duration: 8 weeks

Skills:
- Algorithms
- Artificial Intelligence
- Basic Math
- Reinforcement Learning
- Research

Curriculum:
- The basics of Markov decision processes and dynamic programming
- The mathematical foundations of tabular reinforcement learning including Monte Carlo and temporal-difference methods
- The fundamentals of reinforcement learning with function approximations such as linear models or deep neural networks
- Insights into influential modern deep reinforcement learning algorithms

- Implementing reinforcement learning algorithms using Python