0

Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Hands-On Reinforcement Learning with Python

You're reading from Hands-On Reinforcement Learning with Python Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788836524

Length 318 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (16) Chapters

Preface

1. Introduction to Reinforcement Learning FREE CHAPTER

2. Getting Started with OpenAI and TensorFlow

3. The Markov Decision Process and Dynamic Programming

4. Gaming with Monte Carlo Methods

5. Temporal Difference Learning

6. Multi-Armed Bandit Problem

7. Deep Learning Fundamentals

8. Atari Games with Deep Q Network

9. Playing Doom with a Deep Recurrent Q Network

10. The Asynchronous Advantage Actor Critic Network

11. Policy Gradients and Optimization

12. Capstone Project – Car Racing Using DQN

13. Recent Advancements and Next Steps

14. Assessments

15. Other Books You May Enjoy

Leave a review - let other readers know what you think

Further reading

The following links will help expand your knowledge:

Flappy Bird using DQN: https://github.com/yenchenlin/DeepLearningFlappyBird
Super Mario using DQN: https://github.com/JSDanielPark/tensorflow_dqn_supermario

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at €18.99/month. Cancel anytime

Authors (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

See other products by Sudharsan Ravichandiran

Other recommended products

Related to this chapter

Deep Reinforcement Learning with Python

Deep Reinforcement Learning with Python

Deep Reinforcement Learning with Python - Second Edition will help you learn reinforcement learning algorithms, techniques and architectures – including deep reinforcement learning – from scratch. This new edition is an extensive update of the original, reflecting the state-of-the-art latest thinking in reinforcement learning.

Sep 2020 25h 20m

PyTorch 1.x Reinforcement Learning Cookbook

PyTorch 1.x Reinforcement Learning Cookbook

This book presents practical solutions to the most common reinforcement learning problems. The recipes in this book will help you understand the fundamental concepts to develop popular RL algorithms. You will gain practical experience in the RL domain using the modern offerings of the PyTorch 1.x library.

Oct 2019 11h 20m

Hands-On Deep Learning Algorithms with Python

Hands-On Deep Learning Algorithms with Python

This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such as TensorFlow.

Jul 2019 17h 4m

TensorFlow Reinforcement Learning Quick Start Guide

TensorFlow Reinforcement Learning Quick Start Guide

This book is an essential guide for anyone interested in Reinforcement Learning. The book provides an actionable reference for Reinforcement Learning algorithms and their applications using TensorFlow and Python. It will help readers leverage the power of algorithms such as Deep Q-Network (DQN), Deep Deterministic Policy Gradients (DDPG), and Proximal Policy Optimization (PPO) to solve challenging control and decision-making problems.

Reinforcement Learning with TensorFlow

Reinforcement Learning with TensorFlow

Reinforcement learning allows you to develop intelligent, self-learning systems. This book shows you how to put the concepts of Reinforcement Learning to train efficient models.You will use popular reinforcement learning algorithms to implement use-cases in image processing and NLP, by combining the power of TensorFlow and OpenAI Gym.

Apr 2018 11h 8m

Practical Reinforcement Learning

Practical Reinforcement Learning

Reinforcement learning (RL) is becoming a popular tool for constructing autonomous systems that improve themselves with experience. We will break the RL framework into its core building blocks, and provide you with details of each element. This book is divided into three parts. The first part defines Reinforcement Learning and describes the basics and the Python and Java frameworks, which we are going to use later in the book. The second part discusses learning techniques with basic algorithms such as Temporal Difference, Monte Carlo, and Policy Gradient—all with practical examples. Lastly, in the third part we apply Reinforcement Learning with the most recent and widely used algorithms, via practical applications.

Oct 2017 11h 12m

TensorFlow 2 Reinforcement Learning Cookbook

TensorFlow 2 Reinforcement Learning Cookbook

This cookbook will help you to gain a solid understanding of deep reinforcement learning (RL) algorithms with the help of concise, easy-to-follow implementations from scratch. You'll learn how to implement these algorithms with minimal code and develop AI applications to solve real-world and business problems using RL.

Jan 2021 15h 44m

The Reinforcement Learning Workshop

The Reinforcement Learning Workshop

With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning's core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease.

Aug 2020 27h 24m

Hands-On Intelligent Agents with OpenAI Gym

Hands-On Intelligent Agents with OpenAI Gym

Walks through the hands-on process of building intelligent agents from the basics and all the way up to solving complex problems including playing Atari games and driving a car autonomously in the CARLA simulator. Discusses various learning environments and how to transform real-world problems into learning environments and solve using the agents.

Jul 2018 8h 28m

Hands-On Meta Learning with Python

Hands-On Meta Learning with Python

This hands-on guide for meta learning starts with exploring the principles, algorithms, and implementations of Meta learning with Tensorflow, Keras, and Python. Once it sets the foundation of "learning to learn", the book will help you implement your meta learning algorithms from scratch.

Dec 2018 7h 32m

Hands-On Q-Learning with Python

Hands-On Q-Learning with Python

Q-learning is the reinforcement learning approach behind Deep-Q-Learning and is a values-based learning algorithm in RL. This book will help you get comfortable with developing the effective agents for Q learning and also make you learn to effectively develop and deploy Deep Q networks for complex AI applications.

Mastering Reinforcement Learning with Python

Mastering Reinforcement Learning with Python

This book focuses on expert-level explanations and implementations of scalable reinforcement learning algorithms and approaches. Starting with the fundamentals, the book covers state-of-the-art methods from bandit problems to meta-reinforcement learning. You’ll also explore practical examples inspired by real-life problems from the industry.

Dec 2020 18h 8m

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Data Governance Handbook

Data Governance Handbook

This book provides a highly focused view of real business outcomes powered by data governance, that resonate with non-data executives such as CFOs and CEOs. You'll also find useful insights into how to implement data governance initiatives.

May 2024 13h 12m

Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

This book shows you how to use Apache Spark, Delta Lake, and Databricks to build data pipelines, manage and transform data, optimize performance, and more. Additionally, you'll implement DataOps and DevOps practices, and orchestrate data workflows.

May 2024 14h 36m

Azure Data Engineer Associate Certification Guide

Azure Data Engineer Associate Certification Guide

Unlock the power of Azure data engineering with this certification guide, elevating your skills in data processing, storage, and security with the help of practical insights, hands-on exercises, and the latest advancements.

May 2024 18h 16m

Microsoft Power BI Cookbook

Microsoft Power BI Cookbook

Microsoft Power BI is the most sought-after platform for BI professionals' visualization needs. Explore the latest Power BI features, future AI enhancements, and integration with other Power Platform tools via new recipes in this updated edition.

Jul 2024 19h 56m

Python Data Cleaning Cookbook

Python Data Cleaning Cookbook

The book shows you how to clean, wrangle, and view data from multiple perspectives, including dataset and column attributes. You will cover common and not-so-common challenges that are faced while cleaning messy data for complex situations and learn to manipulate data to get it down to a form that can be useful for making the right decisions.

May 2024 16h 12m

Microsoft Azure AI Fundamentals AI-900 Exam Guide

Microsoft Azure AI Fundamentals AI-900 Exam Guide

This AI-900 study guide will help you prepare and practice for the certification exam. You'll delve into AI workloads, ML principles, computer vision, NLP, knowledge mining, and generative AI using Azure cloud services.

May 2024 9h 36m

Using Stable Diffusion with Python

Using Stable Diffusion with Python

This book shows you how to use Python to control Stable Diffusion and generate high-quality images. In addition to covering the basic usage of the diffusers package, the book provides solutions for extending the package for more advanced purposes.

Jun 2024 11h 44m

Getting Started with DuckDB

Getting Started with DuckDB

This hands-on book teaches you to analyze large datasets with blazing speed and ease. You will learn how to use DuckDB to quickly load, query, transform, analyze, and visualize data effectively through a series of practical examples.

Jun 2024 12h 44m

Databricks Certified Associate Developer for Apache Spark Using Python

Databricks Certified Associate Developer for Apache Spark Using Python

This guide gets you ready for certification with expert-backed content, key exam concepts, and topic reviews. Additionally, you'll be able to make the most of Apache Spark 3.0 to modernize workloads and more using specific tools and techniques.