Reinforcement Learning is the learning of mapping from situation to action to maximize a scalar reward or reinforcement signal. In contrast to Machine Learning, the learner is not told which action to take but must instead discover which actions yield the highest reward by trying them. In the most exciting cases, actions may affect not only the immediate reward but also the next situation and through that, all subsequent rewards. Trial-and-error search and delayed reward – are the two most significant distinguishing properties of Reinforcement Learning.

During this webinar, we will discuss how to apply Reinforcement Learning using MATLAB® and Simulink® products.

Agenda

• Brief Introduction to Reinforcement Learning
• Reinforcement Learning Workflow in MATLAB
• How to train your Bipod Robot to Walk
• Pros & Cons of Reinforcement Learning
• Deep Learning in Medical Imaging