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Python Reinforcement Learning Projects

Python Reinforcement Learning Projects

By : Sean Saito, Yang Wenzhuo , Shanmugamani
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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 preparation

Careful readers may notice that a suffix, v0, follows each game name, and come up with the following questions: What is the meaning of v0? Is it allowable to replace it with v1 or v2? Actually, this suffix has a relationship with the data preprocessing step for the screen images (observations) extracted from the Atari environment.

There are three modes for each game, for example, Breakout, BreakoutDeterministic, and BreakoutNoFrameskip, and each mode has two versions, for example, Breakout-v0 and Breakout-v4. The main difference between the three modes is the value of the frameskip parameter in the Atari environment. This parameter indicates the number of frames (steps) the one action is repeated on. This is called the frame-skipping technique, which allows us to play more games without significantly increasing the runtime.

For Breakout, frameskip is randomly...

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