Reinforcement Learning with MATLAB and Simulink

Reinforcement learning is a type of machine learning that enables the use of artificial intelligence in complex applications from video games to robotics, self-driving cars, and more.

If you are interested in using reinforcement learning technology for your project, but you’ve never used it before, where do you begin?


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In this ebook, you will learn:

illustration, interface, right, ui, user, web Section 1: The Basics and Setting Up the Environment  - Learn the basics of reinforcement learning and how it compares with traditional control design. See the difference between supervised, unsupervised, and reinforcement learning, and see how to set up a learning environment in MATLAB and Simulink


illustration, interface, right, ui, user, web Section 2: Rewards and Policy Structures - Learn about exploration and exploitation in reinforcement learning and how to shape reward functions. Explore different options for representing policies including neural networks and how they can be used as function approximators.


illustration, interface, right, ui, user, web Section 3: Understanding Training and Deployment - Learn about the different types of training algorithms, including policy-based, value-based and actor-critic methods. Find out more about the pros and cons of each training method as well as the popular Bellman equation. Finally, see what you should consider before deploying a trained policy, and overall challenges and drawbacks associated with this technique.
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