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


Autonomous robots and vehicles are going to play a huge role in the future of our world; and reinforcement learning is one of the primary approaches to create such autonomous systems. In this chapter, we have taken a peek at what it looks like to train a robot to accomplish an object grasping task, a major challenge in robotics with many applications in manufacturing and warehouse material handling. We used the PyBullet physics simulator to train a Kuka robot in a hard-exploration setting, for which we leveraged both manual and ALP-GMM-based curriculum learning. Now that you have a fairly good grasp of how to utilize these techniques, you can take on other similar problems.

In the next chapter, we will look into another major area for reinforcement learning applications: Supply chain management. Stay tuned for another exciting journey!