Reinforcement Learning for Decision Making in Complex Environments

In the previous chapters, we focused on supervised and unsupervised machine learning. We also learned how to leverage artificial neural networks and deep learning to tackle problems encountered with these types of machine learning. As you'll recall, supervised learning focuses on predicting a category label or continuous value from a given input feature vector. Unsupervised learning focuses on extracting patterns from data, making it useful for data compression (Chapter 5, Compressing Data via Dimensionality Reduction), clustering (Chapter 11, Working with Unlabeled Data – Clustering Analysis), or approximating the training set distribution for generating new data (Chapter 17, Generative Adversarial Networks for Synthesizing New Data).

In this chapter, we turn our attention to a separate category of machine learning, reinforcement learning (RL), which is different from the previous categories as...