Layered Learning in Multiagent Systems

A Winning Approach to Robotic Soccer

Autor: Peter Stone

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Legare: Copertă tare

Editura: MIT Press Ltd

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This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time...

Informații despre carte

Autor

Peter Stone

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2000

Pagini

284

EAN

9780262194389

ISBN

0262194384

Enbook ID

04561242

Editura

MIT Press Ltd

Greutate

635

Dimensiuni

157 x 229 x 29

Descriere completă

This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. The book makes four main contributions to the fields of machine learning and multiagent systems.First, it describes an architecture within which a flexible team structure allows member agents to decompose a task into flexible roles and to switch roles while acting. Second, it presents layered learning, a general-purpose machine-learning method for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable with existing machine-learning methods. Third, the book introduces a new multiagent reinforcement learning algorithm--team-partitioned, opaque-transition reinforcement learning (TPOT-RL)--designed for domains in which agents cannot necessarily observe the state-changes caused by other agents' actions. The final contribution is a fully functioning multiagent system that incorporates learning in a real-time, noisy domain with teammates and adversaries--a computer-simulated robotic soccer team.Peter Stone's work is the basis for the CMUnited Robotic Soccer Team, which has dominated recent RoboCup competitions. RoboCup not only helps roboticists to prove their theories in a realistic situation, but has drawn considerable public and professional attention to the field of intelligent robotics. The CMUnited team won the 1999 Stockholm simulator competition, outscoring its opponents by the rather impressive cumulative score of 110-0.

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Layered Learning in Multiagent Systems

A Winning Approach to Robotic Soccer

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