How Long to Read Hands-On Reinforcement Learning with R

How Long Does it Take to Read Hands-On Reinforcement Learning with R?

It takes the average reader 6 hours and 4 minutes to read Hands-On Reinforcement Learning with R by Giuseppe Ciaburro

Assuming a reading speed of 250 words per minute. Learn more

Description

Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym Key Features Explore the design principles of reinforcement learning and deep reinforcement learning models Use dynamic programming to solve design issues related to building a self-learning system Learn how to systematically implement reinforcement learning algorithms Book Description Reinforcement learning (RL) is an integral part of machine learning (ML), and is used to train algorithms. With this book, you'll learn how to implement reinforcement learning with R, exploring practical examples such as using tabular Q-learning to control robots. You'll begin by learning the basic RL concepts, covering the agent-environment interface, Markov Decision Processes (MDPs), and policy gradient methods. You'll then use R's libraries to develop a model based on Markov chains. You will also learn how to solve a multi-armed bandit problem using various R packages. By applying dynamic programming and Monte Carlo methods, you will also find the best policy to make predictions. As you progress, you'll use Temporal Difference (TD) learning for vehicle routing problem applications. Gradually, you'll apply the concepts you've learned to real-world problems, including fraud detection in finance, and TD learning for planning activities in the healthcare sector. You'll explore deep reinforcement learning using Keras, which uses the power of neural networks to increase RL's potential. Finally, you'll discover the scope of RL and explore the challenges in building and deploying machine learning models. By the end of this book, you'll be well-versed with RL and have the skills you need to efficiently implement it with R. What you will learn Understand how to use MDP to manage complex scenarios Solve classic reinforcement learning problems such as the multi-armed bandit model Use dynamic programming for optimal policy searching Adopt Monte Carlo methods for prediction Apply TD learning to search for the best path Use tabular Q-learning to control robots Handle environments using the OpenAI library to simulate real-world applications Develop deep Q-learning algorithms to improve model performance Who this book is for This book is for anyone who wants to learn about reinforcement learning with R from scratch. A solid understanding of R and basic knowledge of machine learning are necessary to grasp the topics covered in the book.

How long is Hands-On Reinforcement Learning with R?

Hands-On Reinforcement Learning with R by Giuseppe Ciaburro is 362 pages long, and a total of 91,224 words.

This makes it 122% the length of the average book. It also has 111% more words than the average book.

How Long Does it Take to Read Hands-On Reinforcement Learning with R Aloud?

The average oral reading speed is 183 words per minute. This means it takes 8 hours and 18 minutes to read Hands-On Reinforcement Learning with R aloud.

What Reading Level is Hands-On Reinforcement Learning with R?

Hands-On Reinforcement Learning with R is suitable for students ages 12 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy Hands-On Reinforcement Learning with R?

Hands-On Reinforcement Learning with R by Giuseppe Ciaburro is sold by several retailers and bookshops. However, Read Time works with Amazon to provide an easier way to purchase books.

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