Reinforcement learning (RL) has been gaining attention as a machine learning technique that can automatically learn complex behaviors and realize high performance. RL applications span various domains, including control design, robotics, automated driving, communications, and more. However, reinforcement learning comes with several challenges. These include the need for large amounts of training data, difficulties in tuning hyperparameters, and verification of deep neural network policies.

In this talk, we will discuss trends, applications, and challenges we have observed from our customer interactions at MathWorks. We will introduce ideas, tools, and best practices on how to address these challenges, helping to solve real-world problems with reinforcement learning.