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Reinforcement Learning with TensorFlow

Reinforcement Learning with TensorFlow

By : Dutta
2.2 (5)
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Reinforcement Learning with TensorFlow

Reinforcement Learning with TensorFlow

2.2 (5)
By: Dutta

Overview of this book

Reinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions. The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.
Table of Contents (17 chapters)
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Preface
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Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Deep Learning – Architectures and Frameworks
Deep Learning – Architectures and Frameworks
Deep learning
Reinforcement learning
Introduction to TensorFlow and OpenAI Gym
The pioneers and breakthroughs in reinforcement learning
Summary
2
Training Reinforcement Learning Agents Using OpenAI Gym
Training Reinforcement Learning Agents Using OpenAI Gym
The OpenAI Gym
Programming an agent using an OpenAI Gym environment
Summary
3
Markov Decision Process
Markov Decision Process
Markov decision processes
Partially observable Markov decision processes
Training the FrozenLake-v0 environment using MDP
Summary
4
Policy Gradients
Policy Gradients
The policy optimization method
Why policy optimization methods?
Policy objective functions
Temporal difference rule
Policy gradients
Agent learning pong using policy gradients
Summary
5
Q-Learning and Deep Q-Networks
Q-Learning and Deep Q-Networks
Why reinforcement learning?
Model based learning and model free learning
Q-learning
Deep Q-networks
The Monte Carlo tree search algorithm
The SARSA algorithm
Summary
6
Asynchronous Methods
Asynchronous Methods
Why asynchronous methods?
Asynchronous one-step Q-learning
Asynchronous one-step SARSA
Asynchronous n-step Q-learning
Asynchronous advantage actor critic
A3C for Pong-v0 in OpenAI gym
Summary
7
Robo Everything – Real Strategy Gaming
Robo Everything – Real Strategy Gaming
Real-time strategy games
Reinforcement learning and other approaches
Reinforcement learning in RTS gaming
Summary
8
AlphaGo – Reinforcement Learning at Its Best
AlphaGo – Reinforcement Learning at Its Best
What is Go?
AlphaGo – mastering Go
AlphaGo Zero
Summary
9
Reinforcement Learning in Autonomous Driving
Reinforcement Learning in Autonomous Driving
Machine learning for autonomous driving
Reinforcement learning for autonomous driving
Proposed frameworks for autonomous driving
DeepTraffic – MIT simulator for autonomous driving 
Summary
10
Financial Portfolio Management
Financial Portfolio Management
Introduction
Problem definition
Data preparation
Reinforcement learning
Further improvements
Summary
11
Reinforcement Learning in Robotics
Reinforcement Learning in Robotics
Reinforcement learning in robotics
Challenges in robot reinforcement learning
Open questions and practical challenges
Key takeaways
Summary
12
Deep Reinforcement Learning in Ad Tech
Deep Reinforcement Learning in Ad Tech
Computational advertising challenges and bidding strategies
Real-time bidding by reinforcement learning in display advertising
Summary
13
Reinforcement Learning in Image Processing
Reinforcement Learning in Image Processing
Hierarchical object detection with deep reinforcement learning
Summary
14
Deep Reinforcement Learning in NLP
Deep Reinforcement Learning in NLP
Text summarization
Text question answering
Summary
15
Further topics in Reinforcement Learning
Further topics in Reinforcement Learning
Continuous action space algorithms
Scoring mechanism in sequential models in NLP
Summary
16
Other Books You May Enjoy
Other Books You May Enjoy
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To get the most out of this book

The following are the requirements to get the most out of this book:

  • Python and TensorFlow
  • Linear algebra as a prerequisite for neural networks
  • Installation bundle: Python, TensorFlow, and OpenAI gym (shown in Chapter 1, Deep Learning – Architectures and Frameworks and Chapter 2, Training Reinforcement Learning Agents Using OpenAI Gym)

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packtpub.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Reinforcement-Learning-with-TensorFlow. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The sigmoid(x) and relu(x) refer to the functions performing sigmoid and ReLU activation calculations respectively."

A block of code is set as follows:

def discretization(env, obs):
    
    env_low = env.observation_space.low
    env_high = env.observation_space.high

Any command-line input or output is written as follows:

Episode 1 completed with total reward 8433.30289388 in 26839 steps
Episode 2 completed with total reward 3072.93369963 in 8811 steps
Episode 3 completed with total reward 1230.81734028 in 4395 steps
Episode 4 completed with total reward 2182.31111239 in 6629 steps

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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