Description
Chapter 1: Introduction to Reinforcement LearningChapter Goal: Inform the reader of the history of the field, its current applications, as well as generally discussing the outline of the text and what the reader can expect to learn No of pages 10Sub -Topics1. What is reinforcement learning? 2. History of reinforcement learning 3. Applications of reinforcement learning
Chapter 2: Reinforcement Learning AlgorithmsChapter Goal: Establishing an understanding with the reader about how reinforcement learning algorithms work and how they differ from basic ML/DL methods. Practical examples to be provided for this chapter
No of pages: 50
Sub - Topics 1. Tabular solution methods2. Approximate solution methods
Chapter 3: Q Learning Chapter Goal: In this chapter, readers will continue to build on their understanding of RL by solving problems in discrete action spaces No of pages: 40 Sub - Topics: 1. Deep Q networks2. Double deep Q learning
Chapter 4: Reinforcement Learning Based Market Making Chapter Goal: In this chapter, we will focus on a financial based use case, specifically market making, in which we must buy and sell a financial instrument at any given price. We will apply a reinforcement learning approach to this data set and see how it performs over time No of pages: 50Sub - Topics: 1. Market making 2. AWS/Google Cloud3. Cron
Chapter 5: Reinforcement Learning for Video Games Chapter Goal: In this chapter, we will focus on a more generalized use case of reinforcement learning in which we teach an algorithm to successfully play a game against computer based AI. No of pages: 50Sub - Topics: 1. Game background and data collection
Author: Taweh Beysolow II
Publisher: Apress
Published: 08/24/2019
Pages: 168
Binding Type: Paperback
Weight: 0.59lbs
Size: 9.21h x 6.14w x 0.40d
ISBN13: 9781484251263
ISBN10: 1484251261
BISAC Categories:
- Computers | Artificial Intelligence | General
- Computers | Languages | Python
- Computers | Programming | Open Source
Chapter 2: Reinforcement Learning AlgorithmsChapter Goal: Establishing an understanding with the reader about how reinforcement learning algorithms work and how they differ from basic ML/DL methods. Practical examples to be provided for this chapter
No of pages: 50
Sub - Topics 1. Tabular solution methods2. Approximate solution methods
Chapter 3: Q Learning Chapter Goal: In this chapter, readers will continue to build on their understanding of RL by solving problems in discrete action spaces No of pages: 40 Sub - Topics: 1. Deep Q networks2. Double deep Q learning
Chapter 4: Reinforcement Learning Based Market Making Chapter Goal: In this chapter, we will focus on a financial based use case, specifically market making, in which we must buy and sell a financial instrument at any given price. We will apply a reinforcement learning approach to this data set and see how it performs over time No of pages: 50Sub - Topics: 1. Market making 2. AWS/Google Cloud3. Cron
Chapter 5: Reinforcement Learning for Video Games Chapter Goal: In this chapter, we will focus on a more generalized use case of reinforcement learning in which we teach an algorithm to successfully play a game against computer based AI. No of pages: 50Sub - Topics: 1. Game background and data collection
Author: Taweh Beysolow II
Publisher: Apress
Published: 08/24/2019
Pages: 168
Binding Type: Paperback
Weight: 0.59lbs
Size: 9.21h x 6.14w x 0.40d
ISBN13: 9781484251263
ISBN10: 1484251261
BISAC Categories:
- Computers | Artificial Intelligence | General
- Computers | Languages | Python
- Computers | Programming | Open Source
About the Author
Taweh Beysolow II is a data scientist and author currently based in the United States. He has a Bachelor of Science degree in economics from St. Johns University and a Master of Science in Applied Statistics from Fordham University. After successfully exiting the startup he co-founded, he now is a Director at Industry Capital, a San Francisco based Private Equity firm, where he helps lead the Cryptocurrency and Blockchain platforms.