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

Open source implementations of policy-gradient methods

In this chapter we have covered many algorithms. It is not quite possible to explicitly implement all these algorithms given the space limitations here. We instead relied on RLlib implementations to train agents for our use case. RLlib is open source, so you can go to and dive into implementations of these algorithms.

Having said that, RLlib implementations is built for production systems and therefore involve many other implementations regarding error-handling, and preprocessing. In addition, there is a lot of code reuse, resulting in implementations of with multiple class inheritances. A much easier set of implementations is provided by OpenAI's Spinning Up repo at I highly recommend you go into that repo and dive into the implementation details of these algorithms we discussed in this chapter.


OpenAI Spinning...