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Author:   Michael Hu
Publisher:   APress
Imprint:   APress
Edition:   1st ed.
Weight:   0.588kg
ISBN:  

9781484296059

ISBN 10:   1484296052
Pages:   287
Publication Date:   09 December 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Part I: Foundation.- Chapter 1: Introduction to Reinforcement Learning.- Chapter 2: Markov Decision Processes.- Chapter 3: Dynamic Programming.- Chapter 4: Monte Carlo Methods.- Chapter 5: Temporal Difference Learning.- Part II: Value Function Approximation.- Chapter 6: Linear Value Function Approximation.- Chapter 7: Nonlinear Value Function Approximation.- Chapter 8: Improvement to DQN.-  Part III: Policy Approximation.- Chapter 9: Policy Gradient Methods.- Chapter 10: Problems with Continuous Action Space.- Chapter 11: Advanced Policy Gradient Methods.-  Part IV: Advanced Topics.- Chapter 12: Distributed Reinforcement Learning.- Chapter 13: Curiosity-Driven Exploration.- Chapter 14: Planning with a Model – AlphaZero.

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Author Information

Michael Hu is a skilled software engineer with over a decade of experience in designing and implementing enterprise-level applications. He's a passionate coder who loves to delve into the world of mathematics and has a keen interest in cutting-edge technologies like machine learning and deep learning, with a particular interest in deep reinforcement learning. He has build various open-source projects on Github, which closely mimic the state-of-the-art reinforcement learning algorithms developed by DeepMind, such as AlphaZero, MuZero, and Agent57. Fluent in both English and Chinese, Michael currently resides in the bustling city of Shanghai, China.

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