{"product_id":"multi-agent-coordination-a-reinforcement-learning-approach-hardcover","title":"Multi-Agent Coordination: A Reinforcement Learning Approach - Hardcover","description":"\u003cp\u003eby \u003cb\u003eArup Kumar Sadhu\u003c\/b\u003e (Author), \u003cb\u003eAmit Konar\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\"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.\"--\u003c\/p\u003e\u003ch3\u003eFront Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDiscover the latest developments in multi-robot coordination techniques with this insightful and original resource\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMulti-Agent Coordination: A Reinforcement Learning Approach\u003c\/i\u003e delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. \u003c\/p\u003e\u003cp\u003eYou'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. \u003c\/p\u003e\u003cp\u003eReaders will discover cutting-edge techniques for multi-agent coordination, including: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eAn introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium\u003c\/li\u003e \u003cli\u003eImproving convergence speed of multi-agent Q-learning for cooperative task planning\u003c\/li\u003e \u003cli\u003eConsensus Q-learning for multi-agent cooperative planning\u003c\/li\u003e \u003cli\u003eThe efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning\u003c\/li\u003e \u003cli\u003eA modified imperialist competitive algorithm for multi-agent stick-carrying applications\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, \u003ci\u003eMulti-Agent Coordination: A Reinforcement Learning Approach\u003c\/i\u003e also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eArup Kumar Sadhu, PhD, \u003c\/b\u003e received his doctorate in Multi-Robot Coordination by Reinforcement Learning from Jadavpur University in India in 2017. He works as a scientist with Research \u0026amp; Innovation Labs, Tata Consultancy Services. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAmit Konar, PhD, \u003c\/b\u003e 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.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 320\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.75 x 9 x 6 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 03, 2020\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42710088679487,"sku":"9781119699033","price":243.56,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/986ea3649aaa2d02fcefa5664b038aa2.webp?v=1765053716","url":"https:\/\/dhlswag.com\/products\/multi-agent-coordination-a-reinforcement-learning-approach-hardcover","provider":"BBB","version":"1.0","type":"link"}