Mastering Reinforcement Learning with Python

Chapter 13: Exploring Advanced Topics

In this chapter, we cover several advanced topics in reinforcement learning. First of all, we go deeper into distributed reinforcement learning, in addition to our discussion in the previous chapters, which is a key topic to create scalable training architectures. Next, we present curiosity-driven reinforcement learning to handle hard-exploration problems that are not solvable by traditional exploration techniques. Finally, we discuss offline reinforcement learning, which leverages offline datasets rather than environment interactions to obtain good policies. All of these are hot research areas that you will hear more about over the next several years.

So, in this chapter, you will learn about the following: ...