£173.00

Springer Reinforcement Learning - Engineering and Computer Science

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£175.70 £120.44 £132.49 £144.55 £156.61 £168.67 £180.72 14 March 2026 30 March 2026 15 April 2026 01 May 2026 17 May 2026

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Description

Explore the fundamentals of machine learning with Reinforcement Learning, part of the Springer International Series in Engineering and Computer Science. This text examines the process of learning a mapping from situations to actions to maximize a scalar reward or reinforcement signal. Unlike many other forms of machine learning where a learner is told which action to take, this book focuses on the trial-and-error search required to discover which actions yield the highest rewards. It addresses the complex challenges of delayed rewards, where current actions influence not only immediate outcomes but also future situations and all subsequent rewards. This resource is designed for those studying how agents navigate environments through discovery rather than direct instruction. By understanding these core distinguishing features, readers gain insight into the mechanics of how agents learn to optimize behavior in dynamic settings.

Key Features

Focuses on the core principles of reinforcement learning through trial-and-error search methods.

Explains how learners discover high-reward actions without being told which specific actions to take.

Covers the mechanics of maximizing scalar rewards and reinforcement signals in various situations.

Addresses the challenge of delayed rewards where actions affect both immediate and future states.

Part of the recognized Springer International Series in Engineering and Computer Science.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
31 May 1992
Listed Since
15 February 2007

Barcode

No barcode data available

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