Making Decisions in Complex Environments with Reinforcement Learning

In the previous chapter, we focused on multimodal models for image and text co-learning. The last chapter of this book will be about reinforcement learning, which is the third type of machine learning task mentioned at the beginning of the book. You will see how learning from experience and learning by interacting with the environment differs from previously covered supervised and unsupervised learning.

We will cover the following topics in this chapter:

• Setting up the working environment
• Introducing reinforcement learning with examples
• Solving the FrozenLake environment with dynamic programming
• Performing Monte Carlo learning
• Solving the Taxi problem with the Q-learning algorithm