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Sinopsis
Chapter 1: Introduction to Reinforcement Learning
Chapter 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 10
Sub -Topics
1. What is reinforcement learning?
2. History of reinforcement learning
3. Applications of reinforcement learning
Chapter 2: Reinforcement Learning Algorithms
Chapter 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 methods
2. Approximate solution methods
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 networks
2. 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: 50
Sub - Topics:
1. Market making
2. AWS/Google Cloud
3. 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: 50
Sub - Topics:
1. Game background and data collection
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