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Springer Reinforcement Learning for Decision & Optimal Control - Springer

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Description

Explore the theory that powers the most impressive AI breakthroughs with this comprehensive guide to reinforcement learning (RL). Learn how AlphaZero mastered Go, how autonomous driving systems evolve beyond human skill, and how AlphaStar makes strategic decisions in StarCraft. The book explains how RL enables agents to continuously improve through interaction with their environment, offering a clear path from basic concepts to advanced optimal control techniques. Readers gain practical insight into sequential decision making, self-evolution capabilities, and the mathematical foundations that connect machine learning with control theory. Whether you are a researcher, graduate student, or industry professional, this text provides the tools to design intelligent systems that learn, adapt, and excel in complex, uncertain settings. Published by Springer, the content reflects the latest developments in AI and machine learning, making it an essential addition to any AI library.

Key Features

Understand how reinforcement learning drives self-evolution in agents, enabling continuous skill improvement without explicit programming.

Gain insight into sequential decision processes that underpin breakthroughs like AlphaZero, AlphaStar, and autonomous driving technologies.

Learn optimal control methods that bridge machine learning and classic control theory for robust, real-world applications.

Benefit from clear explanations of complex concepts, making advanced RL techniques accessible to researchers and practitioners alike.

Published by Springer, ensuring authoritative, up-to-date coverage of the latest AI and machine learning advancements.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
07 April 2024
Listed Since
08 April 2024

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