You're reading from Mastering Reinforcement Learning with Python Build next-generation, self-learning models using reinforcement learning techniques and best practices

Product type Paperback

Published in Dec 2020

Publisher Packt

ISBN-13 9781838644147

Length 544 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Reinforcement Learning

Author (1):

Enes Bilgin

View More author details

Table of Contents (24) Chapters

Preface

1. Section 1: Reinforcement Learning Foundations

2. Chapter 1: Introduction to Reinforcement Learning FREE CHAPTER

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

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Getting familiar with the Kuka environment

KUKA is a company that offers industrial robotics solutions, which are widely used in manufacturing and assembly environments. PyBullet includes a simulation of a KUKA robot, used for object grasping simulations (Figure 14.3).

Figure 14.3 – KUKA robots are widely used in industry. (a) A real KUKA robot (image source CNC Robotics website), (b) a PyBullet simulation.

There are multiple Kuka environments in PyBullet for:

Grasping a rectangle block using robot and object position and angles,
Grasping a rectangle block using camera inputs,
Grasping random objects using camera/position inputs.

In this chapter, we focus on the first one, which we look into next in more detail.

Grasping a rectangle block using a Kuka robot

In this environment, the goal of the robot is to reach a rectangle object, grasp it, and raise it up to a certain height. An example scene from the environment, along...