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Springer Reinforcement Learning for Sequential Decision Making

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£61.84 £57.29 £58.28 £59.27 £60.27 £61.26 £62.25 28 July 2026 02 August 2026 08 August 2026 14 August 2026 20 August 2026
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Description

Explore the mechanics behind some of the most significant technical breakthroughs in artificial intelligence. This book by Springer examines reinforcement learning (RL), the core theory that allows agents to develop self-evolution capabilities through continuous interaction with their environments. By studying this text, you can understand how systems like AlphaZero achieve victory against top human Go players and how AlphaStar manages complex decisions in Starcraft. The content also addresses the foundational principles used in developing autonomous driving systems that improve their skills over time through environmental feedback. This resource is designed for those looking to grasp how agents learn to navigate partially informed environments and complex rule sets. It provides a theoretical framework for understanding how continuous interaction leads to advanced decision-making and optimal control in modern AI applications.

Key Features

Understand the core mechanisms of reinforcement learning that drive modern AI breakthroughs.

Learn how agents develop self-evolution abilities through continuous environmental interactions.

Gain insights into the technology behind AlphaZero and its success in strategic games.

Explore the principles used to develop autonomous driving and self-driving skills.

Study decision-making processes in complex environments with partial information like AlphaStar.

Product Specifications

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

Barcode

No barcode data available

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