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Python Reinforcement Learning Projects

Python Reinforcement Learning Projects

By : Sean Saito, Yang Wenzhuo , Shanmugamani
5 (1)
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Python Reinforcement Learning Projects

Python Reinforcement Learning Projects

5 (1)
By: Sean Saito, Yang Wenzhuo , Shanmugamani

Overview of this book

Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.
Table of Contents (12 chapters)
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Data used


The data that we will use will be the standard and poor's 500. According to Wikipedia, it is An American stock market index based on the market capitalizations of 500 large companies having common stock listed on the NYSE or NASDAQ. Here is a link to the data (https://ca.finance.yahoo.com/quote/%255EGSPC/history?p=%255EGSPC).

The data has the following columns:

  1. Date: This indicates the date under consideration
  2. Open: This indicates the price at which the market opens on the date
  3. High: This indicates the highest market price on the date
  4. Low: This indicates the lowest market price on the date
  5. Close: This indicates the price at which the market closes on the date, adjusted for the split
  6. Adj Close: This indicates the adjusted closing price for both the split and dividends
  7. Volume: This indicates the total volume of shares available

The date under consideration for training the data is as follows:

Start: 14 August 2006
End: 13th August 2015

On the website, filter the date as follows, and download...

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