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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

Concepts

Reinforcement Learning

Author (1):

Enes Bilgin

Table of Contents (24) Chapters

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

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Developing strategies to solve the Kuka environment

The object grasping problem in the environment is a hard-exploration problem, meaning that it is unlikely to stumble upon the sparse reward that the agent receives at the end upon grasping the object. Reducing the vertical speed as we will do will make is a bit easier. Still, let's refresh our minds about what strategies we have covered to address these kinds of problems:

Reward shaping is one of the most common machine teaching strategies that we discussed earlier. In some problems, incentivizing the agent towards the goal is very straightforward. In many problems, though, it can be quite painful. So, unless there is an obvious way of doing so, crafting the reward function may just take too much time (and expertise about the problem). Also notice that the original reward function has a component to penalize the distance between the gripper and the object, so the reward is already shaped to some extent. We will not go...

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Authors (1)

Enes Bilgin

Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.

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