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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
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.
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.
