Book Image

Mastering Reinforcement Learning with Python

By : Enes Bilgin
Book Image

Mastering Reinforcement Learning with Python

By: Enes Bilgin

Overview of this book

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.
Table of Contents (24 chapters)
Section 1: Reinforcement Learning Foundations
Section 2: Deep Reinforcement Learning
Section 3: Advanced Topics in RL
Section 4: Applications of RL

Chapter 18: Challenges and Future Directions in Reinforcement Learning

In this last chapter, we summarize our journey that is coming to an end in this book: You have done a lot, so think of this as a celebration and a bird eye view of your achievement! On the other hand, when you take your learnings to use reinforcement learning in real-world problems, you will likely encounter many challenges. Thankfully, deep reinforcement learning is a fast-moving field with a lot of progress to address those challenges. We have already mentioned most of them in the book and implemented solution approaches. In this chapter, we will recap what those challenges and corresponding future directions in RL are. We will wrap up the chapter and the book by going over some resources and strategies for you to deepen your expertise in RL.

So, here is what you will read in this chapter:

  • What you have achieved with this book
  • Challenges and future directions
  • Suggestions for aspiring reinforcement...