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

Sudharsan Ravichandiran

ISBN 10: 1788836529 / ISBN 13: 9781788836524

Edité par Packt Publishing, 2018

A propos de cet article

Hands-On Reinforcement Learning with Python | Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow | Sudharsan Ravichandiran | Taschenbuch | Englisch | 2018 | Packt Publishing | EAN 9781788836524 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

N° de réf. du vendeur 114091218

Détails bibliographiques

• Titre: Hands-On Reinforcement Learning with Python | Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
• Éditeur: Packt Publishing
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 1788836529
• ISBN à 13 chiffres: 9781788836524
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 191 H x 18 P (en millimètres) 235 millimètres de largeur par 191 millimètres de hauteur par 18 millimètres de profondeur
• Poids: 597 grammes
• Catalogues du vendeur:
  • Bücher

A propos de ce titre

Synopsis

A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python

Key Features:

• 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

Book 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.

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

Who this book is 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.

Présentation de l'éditeur

A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python

Key Features

• Enter 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

Book Description

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence (AI). Hands-On Reinforcement Learning with Python will help you master not only basic reinforcement learning algorithms but also advanced deep reinforcement learning (DRL) 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 this 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.

What you will learn

• Understand the basics of RL methods, algorithms, and elements
• Train an agent to walk using OpenAI Gym and Tensorflow
• Understand Markov decision process, Bellman's optimality, and temporal difference (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

Who This Book Is For

Hands-On Reinforcement Learning with Python is for machine learning developers and deep learning enthusiasts interested in artificial intelligence and want to learn about reinforcement learning from scratch. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

Table of Contents

1. Introduction to Reinforcement Learning
2. Getting Started with OpenAI and Tensorflow
3. Markov Decision Process and Dynamic Programming
4. Gaming with Monte Carlo Tree Search
5. Temporal Difference Learning
6. Multi-Armed Bandit Problem
7. Deep Learning Fundamentals
8. Deep Learning and Reinforcement
9. Playing Doom With Deep Recurrent Q Network
10. Asynchronous Advantage Actor Critic Network
11. Policy Gradients and Optimization
12. Capstone Project Car Racing using DQN