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Mastering Reinforcement Learning with Python

You're reading from  Mastering Reinforcement Learning with Python

Product type Book
Published in Dec 2020
Publisher Packt
ISBN-13 9781838644147
Pages 544 pages
Edition 1st Edition
Languages
Author (1):
Enes Bilgin Enes Bilgin
Profile icon Enes Bilgin
Toc

Table of Contents (24) Chapters close

Preface 1. Section 1: Reinforcement Learning Foundations
2. Chapter 1: Introduction to Reinforcement Learning 3. Chapter 2: Multi-Armed Bandits 4. Chapter 3: Contextual Bandits 5. Chapter 4: Makings of a Markov Decision Process 6. Chapter 5: Solving the Reinforcement Learning Problem 7. Section 2: Deep Reinforcement Learning
8. Chapter 6: Deep Q-Learning at Scale 9. Chapter 7: Policy-Based Methods 10. Chapter 8: Model-Based Methods 11. Chapter 9: Multi-Agent Reinforcement Learning 12. Section 3: Advanced Topics in RL
13. Chapter 10: Introducing Machine Teaching 14. Chapter 11: Achieving Generalization and Overcoming Partial Observability 15. Chapter 12: Meta-Reinforcement Learning 16. Chapter 13: Exploring Advanced Topics 17. Section 4: Applications of RL
18. Chapter 14: Solving Robot Learning 19. Chapter 15: Supply Chain Management 20. Chapter 16: Personalization, Marketing, and Finance 21. Chapter 17: Smart City and Cybersecurity 22. Chapter 18: Challenges and Future Directions in Reinforcement Learning 23. Other Books You May Enjoy

Introducing PyBullet

PyBullet is a popular high-fidelity physics simulation for robotics, machine learning, games, and more. It is one of the most commonly used libraries for robot learning using RL, especially in sim-to-real transfer research and applications.

Figure 14.1 – PyBullet environments and visualizations (source: PyBullet GitHub repo)

PyBullet allows developers to create their own physics simulations. In addition, it has prebuilt environments using the OpenAI Gym interface. Some of those environments are shown in Figure 14.1.

In the next section, we will set up a virtual environment for PyBullet.

Setting up PyBullet

It is almost always a good idea to work in virtual environments for Python projects, which is also what we do for our robot learning experiments in this chapter. So, let's go ahead and execute the following commands to install the libraries we will use:

$ virtualenv pybenv
$ source pybenv/bin/activate
$ pip install...
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