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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...

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Antonio Gulli Aug 17, 2018

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

Sam mus Aug 16, 2019

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

Arivarasan.E Dec 29, 2018

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.

Sam Jul 12, 2018

I have not gone through all the chapters yet but this looks promising for detailed knowledge. Great to have this book.

Mark Oct 30, 2019

Very good examples and plain delivery of knowledge. Great for beginners.

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

What do you get with Print?

Hands-On Reinforcement Learning with Python

Getting Started with OpenAI and TensorFlow

Setting up your machine

Installing Anaconda

OpenAI Gym

Basic simulations

OpenAI Universe

Building a video game bot

TensorFlow

Summary

Questions

Further reading

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

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

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Table of Contents

Recommendations for you

Customer reviews

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