Название: Embodied Multi-agent Systems: Perception, Action and Learning
Автор: Huaping Liu, Xinzhu Liu, Kangyao Huang, Di Guo
Издательство: Springer
Серия: Machine Learning: Foundations, Methodologies, and Applications
Год: 2025
Страниц: 246
Язык: английский
Формат: pdf (true)
Размер: 19.7 MB
In recent years, embodied multi-agent systems, including multi-robots, have emerged as essential solution for demanding tasks such as search and rescue, environmental monitoring, and space exploration. Effective collaboration among these agents is crucial but presents significant challenges due to differences in morphology and capabilities, especially in heterogenous systems. While existing books address collaboration control, perception, and learning, there is a gap in focusing on active perception and interactive learning for embodied multi-agent systems. This book aims to bridge this gap by establishing a unified framework for perception and learning in embodied multi-agent systems. It presents and discusses the perception-action-learning loop, offering systematic solutions for various types of agents—homogeneous, heterogeneous, and ad hoc. Beyond the popular Reinforcement Learning techniques, the book provides insights into using fundamental models to tackle complex collaboration problems. By interchangeably utilizing constrained optimization, Reinforcement Learning, and fundamental models, this book offers a comprehensive toolkit for solving different types of embodied multi-agent problems. Readers will gain an understanding of the advantages and disadvantages of each method for various tasks. This book is suitable as a reference book for graduate students with a basic knowledge of Machine Learning, as well as for professional researchers interested in robotics and embodied intelligence.
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