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TensorFlow Reinforcement Learning Quick Start Guide
TensorFlow Reinforcement Learning Quick Start Guide

TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with training and deploying intelligent, self-learning agents using Python

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TensorFlow Reinforcement Learning Quick Start Guide

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Key benefits

  • Explore efficient Reinforcement Learning algorithms and code them using TensorFlow and Python
  • Train Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.
  • Formulate and devise selective algorithms and techniques in your applications in no time.

Description

Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems.

Who is this book for?

Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.

What you will learn

  • Understand the theory and concepts behind modern Reinforcement Learning algorithms
  • Code state-of-the-art Reinforcement Learning algorithms with discrete or continuous actions
  • Develop Reinforcement Learning algorithms and apply them to training agents to play computer games
  • Explore DQN, DDQN, and Dueling architectures to play Atari s Breakout using TensorFlow
  • Use A3C to play CartPole and LunarLander
  • Train an agent to drive a car autonomously in a simulator

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 30, 2019
Length: 184 pages
Edition : 1st
Language : English
ISBN-13 : 9781789533446
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Google
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What do you get with eBook?

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Product Details

Publication date : Mar 30, 2019
Length: 184 pages
Edition : 1st
Language : English
ISBN-13 : 9781789533446
Vendor :
Google
Category :
Tools :

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Frequently bought together


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TensorFlow 2.0 Quick Start Guide
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TensorFlow Reinforcement Learning Quick Start Guide
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Table of Contents

10 Chapters
Up and Running with Reinforcement Learning Chevron down icon Chevron up icon
Temporal Difference, SARSA, and Q-Learning Chevron down icon Chevron up icon
Deep Q-Network Chevron down icon Chevron up icon
Double DQN, Dueling Architectures, and Rainbow Chevron down icon Chevron up icon
Deep Deterministic Policy Gradient Chevron down icon Chevron up icon
Asynchronous Methods - A3C and A2C Chevron down icon Chevron up icon
Trust Region Policy Optimization and Proximal Policy Optimization Chevron down icon Chevron up icon
Deep RL Applied to Autonomous Driving Chevron down icon Chevron up icon
Assessment Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(2 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
Praveen Narayanan Jun 27, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book presents a readable, instructive overview of the latest RL methods for the beginning practitioner. It walks the reader through the subject with motivating examples and well chosen code to get their hands dirty.
Amazon Verified review Amazon
Colbert Philippe Nov 20, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a fantastic book for those starting in the field.
Amazon Verified review Amazon
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