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MULTI AGENT REINFORCEMENT LEARNING

Name: MULTI AGENT REINFORCEMENT LEARNING
Brand: MIT
SKU: 9780262049375
Price: 74.21 EUR
Availability: InStock

Autor: ALBRECHT STEFANO V

Jazyk:

Väzba: Pevná

Vydavateľ: MIT

Dostupnosť: Skladom u dodávateľa v malom množstve

Odosielame za 11-15 dní

74.21 €

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARLs m...

Informácie o knihe

Autor

ALBRECHT STEFANO V

Jazyk

Angličtina

Väzba

Kniha - Pevná

Vydalo

2024

Stránok

394

EAN

9780262049375

Enbook ID

45775744

Vydavateľ

MIT

Hmotnosť

760

Kompletný popis

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARLs models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the fields foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers.

First textbook to introduce the foundations and applications of MARL, written by experts in the field
Integrates reinforcement learning, deep learning, and game theory
Practical focus covers considerations for running experiments and describes environments for testing MARL algorithms
Explains complex concepts in clear and simple language
Classroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics
Resources include code and slides

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