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Springer - Mathematical Foundations of Reinforcement Learning

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Description

Gain a deep understanding of reinforcement learning with this comprehensive guide from Springer. This book provides a mathematical yet accessible introduction to fundamental concepts, core challenges, and classic reinforcement learning algorithms. Designed to help readers grasp the theoretical foundations of algorithms, it offers clear insights into their design and functionality. The content is organized into two distinct parts. The first part focuses on the mathematical foundations, covering essential topics such as the Bellman equation and Bellman principles. To ensure readability and approachability, the mathematical content is carefully structured and supported by numerous illustrative examples throughout the text. This book is an ideal resource for those seeking to master the theory behind modern algorithmic design.

Key Features

This Springer publication provides a mathematical and accessible introduction to core reinforcement learning algorithms.

The text helps readers understand the theoretical foundations and functionality behind algorithm design.

Mathematical content is carefully structured to ensure the material remains readable and approachable for students.

Numerous illustrative examples are included throughout the book to clarify complex reinforcement learning concepts.

The book is divided into two parts, starting with a focus on mathematical foundations like the Bellman equation.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
22 January 2025
Listed Since
08 May 2024

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