How Long to Read Reinforcement Learning and Dynamic Programming Using Function Approximators

How Long Does it Take to Read Reinforcement Learning and Dynamic Programming Using Function Approximators?

It takes the average reader 4 hours and 44 minutes to read Reinforcement Learning and Dynamic Programming Using Function Approximators by Lucian Busoniu

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

Description

From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.

How long is Reinforcement Learning and Dynamic Programming Using Function Approximators?

Reinforcement Learning and Dynamic Programming Using Function Approximators by Lucian Busoniu is 277 pages long, and a total of 71,189 words.

This makes it 93% the length of the average book. It also has 87% more words than the average book.

How Long Does it Take to Read Reinforcement Learning and Dynamic Programming Using Function Approximators Aloud?

The average oral reading speed is 183 words per minute. This means it takes 6 hours and 29 minutes to read Reinforcement Learning and Dynamic Programming Using Function Approximators aloud.

What Reading Level is Reinforcement Learning and Dynamic Programming Using Function Approximators?

Reinforcement Learning and Dynamic Programming Using Function Approximators 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 Reinforcement Learning and Dynamic Programming Using Function Approximators?

Reinforcement Learning and Dynamic Programming Using Function Approximators by Lucian Busoniu 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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