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Hands-On Reinforcement Learning with Python
Hands-On Reinforcement Learning with Python

Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

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Profile Icon Sudharsan Ravichandiran
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Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.6 (18 Ratings)
Paperback Jun 2018 318 pages 1st Edition
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NZ$45.99
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NZ$56.99
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Arrow left icon
Profile Icon Sudharsan Ravichandiran
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Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.6 (18 Ratings)
Paperback Jun 2018 318 pages 1st Edition
eBook
NZ$45.99
Paperback
NZ$56.99
Subscription
Free Trial
eBook
NZ$45.99
Paperback
NZ$56.99
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Free Trial

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Hands-On Reinforcement Learning with Python

Getting Started with OpenAI and TensorFlow

OpenAI is a non-profit, open source artificial intelligence (AI) research company founded by Elon Musk and Sam Altman that aims to build a general AI. They are sponsored by top industry leaders and top-notch companies. OpenAI comes in two flavors, Gym and Universe, using which we can simulate realistic environments, build reinforcement learning (RL) algorithms, and test our agents in those environments. TensorFlow is an open source machine learning library by Google that is extensively used for numerical computation. We will use OpenAI and TensorFlow for building and evaluating powerful RL algorithms in the upcoming chapters.

In this chapter, you will learn about the following:

  • Setting up your machine by installing Anaconda, Docker, OpenAI Gym, and Universe and TensorFlow
  • Simulating an environment using OpenAI Gym and Universe
  • Training...

Setting up your machine

Installing OpenAI is not a straightforward task; there are a set of steps that have to be correctly followed for setting the system up and running it. Now, let's see how to set up our machine and install OpenAI Gym and Universe.

Installing Anaconda

All the examples in the book use the Anaconda version of Python. Anaconda is an open source distribution of Python. It is widely used for scientific computing and processing a large volume of data. It provides an excellent package management environment. It provides support for Windows, macOS, and Linux. Anaconda comes with Python installed along with popular packages used for scientific computing such as NumPy, SciPy, and so on.

To download Anaconda...

OpenAI Gym

With OpenAI Gym, we can simulate a variety of environments and develop, evaluate, and compare RL algorithms. Let's now understand how to use Gym.

Basic simulations

Let's see how to simulate a basic cart pole environment:

  1. First, let's import the library:
import gym
  1. The next step is to create a simulation instance using the make function:
env = gym.make('CartPole-v0')
  1. Then we should initialize the environment using the reset method:
env.reset()
  1. Then we can loop for some time steps and render the environment at each step:
for _ in range(1000):
env.render()
env.step(env.action_space.sample())

The complete code is as follows:

import gym 
env = gym.make(
'CartPole-v0')
env...

OpenAI Universe

OpenAI Universe provides a wide range of realistic gaming environments. It is an extension to OpenAI Gym. It provides the ability to train and evaluate agents on a wide range of simple to real-time complex environments. It has unlimited access to many gaming environments.

Building a video game bot

Let's learn how to build a video game bot which plays a car racing game. Our objective is that the car has to move forward without getting stuck on any obstacles or hitting other cars.

First, we import the necessary libraries:

import gym
import universe # register universe environment
import random

Then we simulate our car racing environment using the make function:

env = gym.make('flashgames.NeonRace-v0...

TensorFlow

TensorFlow is an open source software library from Google which is extensively used for numerical computation. It is widely used for building deep learning models and is a subset of machine learning. It uses data flow graphs that can be shared and executed on many different platforms. Tensor is nothing but a multi-dimensional array, so when we say TensorFlow, it is literally a flow of multi-dimensional arrays (tensors) in the computation graph.

With Anaconda installed, installing TensorFlow becomes very simple. Irrespective of the platform you are using, you can easily install TensorFlow by typing the following command:

source activate universe
conda install -c conda-forge tensorflow
Don't forget to activate the universe environment before installing TensorFlow.

We can check whether the TensorFlow installation was successful by simply running the following Hello...

Summary

In this chapter, we learned how to set up our machine by installing Anaconda, Docker, OpenAI Gym, Universe, and TensorFlow. We also learned how to create simulations using OpenAI and how to train agents to learn in an OpenAI environment. Then we came across the fundamentals of TensorFlow followed by visualizing graphs in TensorBoard.

In the next chapter, Chapter 3, The Markov Decision Process and Dynamic Programming we will learn about Markov Decision Process and dynamic programming and how to solve frozen lake problem using value and policy iteration.

Questions

The question list is as follows:

  1. Why and how do we create a new environment in Anaconda?
  2. What is the need for using Docker?
  3. How do we simulate an environment in OpenAI Gym?
  4. How do we check all available environments in OpenAI Gym?
  5. Are OpenAI Gym and Universe the same? If not, what is the reason?
  6. How are TensorFlow variables and placeholders different from each other?
  7. What is a computational graph?
  8. Why do we need sessions in TensorFlow?
  9. What is the purpose of TensorBoard and how do we start it?

Further reading

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Key benefits

  • •Your entry point into the world of artificial intelligence using the power of Python
  • •An example-rich guide to master various RL and DRL algorithms
  • •Explore various state-of-the-art architectures along with math

Description

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.

Who is this book for?

If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

What you will learn

  • Understand the basics of reinforcement learning methods, algorithms, and elements
  • Train an agent to walk using OpenAI Gym and Tensorflow
  • Understand the Markov Decision Process, Bellman's optimality, and TD learning
  • Solve multi-armed-bandit problems using various algorithms
  • Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
  • Build intelligent agents using the DRQN algorithm to play the Doom game
  • Teach agents to play the Lunar Lander game using DDPG
  • Train an agent to win a car racing game using dueling DQN

Product Details

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Publication date : Jun 28, 2018
Length: 318 pages
Edition : 1st
Language : English
ISBN-13 : 9781788836524
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Product Details

Publication date : Jun 28, 2018
Length: 318 pages
Edition : 1st
Language : English
ISBN-13 : 9781788836524
Category :
Languages :

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Table of Contents

15 Chapters
Introduction to Reinforcement Learning Chevron down icon Chevron up icon
Getting Started with OpenAI and TensorFlow Chevron down icon Chevron up icon
The Markov Decision Process and Dynamic Programming Chevron down icon Chevron up icon
Gaming with Monte Carlo Methods Chevron down icon Chevron up icon
Temporal Difference Learning Chevron down icon Chevron up icon
Multi-Armed Bandit Problem Chevron down icon Chevron up icon
Deep Learning Fundamentals Chevron down icon Chevron up icon
Atari Games with Deep Q Network Chevron down icon Chevron up icon
Playing Doom with a Deep Recurrent Q Network Chevron down icon Chevron up icon
The Asynchronous Advantage Actor Critic Network Chevron down icon Chevron up icon
Policy Gradients and Optimization Chevron down icon Chevron up icon
Capstone Project – Car Racing Using DQN Chevron down icon Chevron up icon
Recent Advancements and Next Steps Chevron down icon Chevron up icon
Assessments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.6
(18 Ratings)
5 star 22.2%
4 star 5.6%
3 star 22.2%
2 star 11.1%
1 star 38.9%
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Antonio Gulli Aug 17, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
reinforcement learning made simple. Simple solid math when needed, with good python code.Solid introduction to reinforcement learning traditional strategies and modern deep reinforcement learning.Definitively recommend.
Amazon Verified review Amazon
Sam mus Aug 16, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book. Extremely useful. I got the chance to understand how to use RL with clear examples and a solid mathematical background that is suitably explained for Machine Learning practitioners
Amazon Verified review Amazon
Arivarasan.E Dec 29, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book starts with building strong foundations to Reinforcement Learning and then explains deep reinforcement learning algorithms. I really liked the way author has explained advanced concepts in such a simple and more intuitive way. Also, I never know I can understand math so simply. Building Applications like training robot to walk, building car racing agent, lunar lander really makes it fun while learning. Overall awesome book.
Amazon Verified review Amazon
Sam Jul 12, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have not gone through all the chapters yet but this looks promising for detailed knowledge. Great to have this book.
Amazon Verified review Amazon
Mark Oct 30, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Very good examples and plain delivery of knowledge. Great for beginners.
Amazon Verified review Amazon
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