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

Implementing scalable deep Q-learning algorithms using Ray

In this section, we will implement a parallelized DQN variate using the Ray library. Ray is a powerful, general-purpose, yet simple framework for building and running distributed applications on a single machine as well as on large clusters. Ray has been built for applications that have heterogenous computational needs in mind. This is exactly what modern DRL algorithms require as they involve a mix of long and short running tasks, usage of GPU and CPU resources, and more. In fact, Ray itself has a powerful RL library that is called RLlib. Both Ray and RLlib have been increasingly adopted in academia and industry.


For a comparison of Ray to other distributed backend frameworks such as Spark and Dask, see You will see that Ray is a very competitive alternative, even beating Python's own multiprocessing implementation in some benchmarks.

Writing a production-grade distributed application...