Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)

Descripción

Condition: 15 pages with some highlighted text, the rest excellent.

The book provides a clear and simple account of the key ideas and algorithms in this area of artificial intelligence, where an agent learns to maximize a cumulative reward by interacting with a complex, uncertain environment.

It covers the history of the field's intellectual foundations and proceeds to the core algorithms and concepts, including:

The Reinforcement Learning Problem framed in terms of Markov Decision Processes (MDPs).

Basic Solution Methods like Dynamic Programming, Monte Carlo methods, and the influential Temporal-Difference (TD) learning (e.g., Q-learning and SARSA).

Function Approximation for handling large state spaces, including the use of artificial neural networks.

More advanced topics like policy-gradient methods and a discussion of RL's relationships to psychology and neuroscience.

Often referred to as the "bible" of the field, it is a foundational text suitable for students, researchers, and practitioners with a basic understanding of probability.

N° de ref. del artículo ABE-1760107744142

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Detalles bibliográficos

Título

Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)

Editor

Bradford Books

Fecha de publicación

1998

Idioma

Inglés

ISBN 10

0262193981

ISBN 13

9780262193986

Encuadernación

Encuadernación de tapa dura

Edición

1ª Edición

Impresión

1ª Impresión

Estado

Bueno

Dimensiones

3,175 cm an. × 18,415 al. × 24,13 l. 3,175 centímetros de ancho por 18,415 de alto por 24,13 de largo

Peso

794 gramos

Catálogos de vendedores

• Reinforcement learning
• Data science
• Machine Learning

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