"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."--

� propos de l?auteur

Arup Kumar Sadhu, PhD, received his doctorate in Multi-Robot Coordination by Reinforcement Learning from Jadavpur University in India in 2017. He works as a scientist with Research & Innovation Labs, Tata Consultancy Services.

Amit Konar, PhD, received his doctorate from Jadavpur University, India in 1994. He is Professor with the Department of Electronics and Tele-Communication Engineering at Jadavpur University where he serves as the Founding Coordinator of the M. Tech. program on intelligent automation and robotics.

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