Name: Multi-Agent Coordination: A Reinforcement Learning Approach (Wiley - Ieee) (en Inglés)
SKU: 52817183
Price: 2576.64 MXN
Availability: InStock
Author: Konar, Amit ; Sadhu, Arup Kumar
ISBN: 9781119699033

portada Multi-Agent Coordination: A Reinforcement Learning Approach (Wiley - Ieee) (en Inglés)

Multi-Agent Coordination: A Reinforcement Learning Approach (Wiley - Ieee) (en Inglés)

Formato

Libro Físico

Autor

Amit Konar

Arup Kumar Sadhu

Editorial

Wiley-IEEE Press

Año

2020

Idioma

Inglés

N° páginas

320

Encuadernación

Tapa Dura

Dimensiones

22.9x15.2x1.9 cm

Peso

0.59 kg.

ISBN13

9781119699033

N° edición

Categorías

Inteligencia artificial

Multi-Agent Coordination: A Reinforcement Learning Approach (Wiley - Ieee) (en Inglés)

Amit Konar (Autor) · Arup Kumar Sadhu (Autor) · Wiley-IEEE Press · Tapa Dura

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Reseña del libro "Multi-Agent Coordination: A Reinforcement Learning Approach (Wiley - Ieee) (en Inglés)"

This book explores the usage of Reinforcement Learning for Multi-Agent Coordination. Chapter 1 introduces fundamentals of the multi-robot coordination. Chapter 2 offers two useful properties, which have been developed to speed-up the convergence of traditional multi-agent Q-learning (MAQL) algorithms in view of the team-goal exploration, where team-goal exploration refers to simultaneous exploration of individual goals. Chapter 3 proposes the novel consensus Q-learning (CoQL), which addresses the equilibrium selection problem. Chapter 4 introduces a new dimension in the literature of the traditional correlated Q-learning (CQL), in which correlated equilibrium (CE) is computed partly in the learning and the rest in the planning phases, thereby requiring CE computation once only. Chapter 5 proposes an alternative solution to the multi-agent planning problem using meta-heuristic optimization algorithms. Chapter 6 provides the concluding remarks based on the principles and experimental results acquired in the previous chapters. Possible future directions of research are also examined briefly at the end of the chapter.