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Book details
File Size: 21 MB
Format: epub
Print Length: 298 pages
Page Numbers Source ISBN: 1788991613
Publisher: Packt Publishing; 1 edition (September 29, 2018)
Publication Date: September 29, 2018
Sold by: Amazon.com Services LLC
Language: English
ASIN: B07F2S82W3

Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries

Key Features



Implement Q-learning and Markov models with Python and OpenAI
Explore the power of TensorFlow to build self-learning models
Eight AI projects to gain confidence in building self-trained applications
Book Description
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.

What you will learn



Train and evaluate neural networks built using TensorFlow for RL
Use RL algorithms in Python and TensorFlow to solve CartPole balancing
Create deep reinforcement learning algorithms to play Atari games
Deploy RL algorithms using OpenAI Universe
Develop an agent to chat with humans
Implement basic actor-critic algorithms for continuous control
Apply advanced deep RL algorithms to games such as Minecraft
Autogenerate an image classifier using RL
Who this book is for
Python Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. Individuals who want to work on self-learning model projects will also find this book useful.

Table of Contents

Up and running with Reinforcement Learning
Balancing Cart Pole
Playing ATARI Games
Simulating Control Tasks
Building Virtual Worlds in Minecraft
Learning to Play Go
Creating a Chatbot
Generating a Deep Learning Image Classifier
Predicting Future Stock Prices
Looking Ahead