Multi Agent Machine Learning

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Multi-Agent Machine Learning

Author : H. M. Schwartz
Publisher : John Wiley & Sons
ISBN 13 : 1118884485
Total Pages : 328 pages
Book Rating : 4.1/5 (188 download)

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Book Synopsis Multi-Agent Machine Learning by : H. M. Schwartz

Download or read book Multi-Agent Machine Learning written by H. M. Schwartz and published by John Wiley & Sons. This book was released on 2014-08-26 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces. Chapter 3 discusses two player games including two player matrix games with both pure and mixed strategies. Numerous algorithms and examples are presented. Chapter 4 covers learning in multi-player games, stochastic games, and Markov games, focusing on learning multi-player grid games—two player grid games, Q-learning, and Nash Q-learning. Chapter 5 discusses differential games, including multi player differential games, actor critique structure, adaptive fuzzy control and fuzzy interference systems, the evader pursuit game, and the defending a territory games. Chapter 6 discusses new ideas on learning within robotic swarms and the innovative idea of the evolution of personality traits. • Framework for understanding a variety of methods and approaches in multi-agent machine learning. • Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning • Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering

Multi-Agent Coordination

Author : Arup Kumar Sadhu
Publisher : John Wiley & Sons
ISBN 13 : 1119699029
Total Pages : 320 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Multi-Agent Coordination by : Arup Kumar Sadhu

Download or read book Multi-Agent Coordination written by Arup Kumar Sadhu and published by John Wiley & Sons. This book was released on 2020-12-01 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach 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. You'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. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach 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.

Multi-agent Systems

Author : Jacques Ferber
Publisher : Addison-Wesley Professional
ISBN 13 :
Total Pages : 536 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Multi-agent Systems by : Jacques Ferber

Download or read book Multi-agent Systems written by Jacques Ferber and published by Addison-Wesley Professional. This book was released on 1999 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, Jacques Ferber has brought together all the recent developments in the field of multi-agent systems - an area that has seen increasing interest and major developments over the last few years. The author draws on work carried out in various disciplines, including information technology, sociology and cognitive psychology to provide a coherent and instructive picture of the current state-of-the-art. The book introduces and defines the fundamental concepts that need to be understood, clearly describes the work that has been done, and invites readers to reflect upon the possibilities of the future.

Rollout, Policy Iteration, and Distributed Reinforcement Learning

Author : Dimitri Bertsekas
Publisher : Athena Scientific
ISBN 13 : 1886529078
Total Pages : 498 pages
Book Rating : 4.8/5 (865 download)

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Book Synopsis Rollout, Policy Iteration, and Distributed Reinforcement Learning by : Dimitri Bertsekas

Download or read book Rollout, Policy Iteration, and Distributed Reinforcement Learning written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 2021-08-20 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to develop in greater depth some of the methods from the author's Reinforcement Learning and Optimal Control recently published textbook (Athena Scientific, 2019). In particular, we present new research, relating to systems involving multiple agents, partitioned architectures, and distributed asynchronous computation. We pay special attention to the contexts of dynamic programming/policy iteration and control theory/model predictive control. We also discuss in some detail the application of the methodology to challenging discrete/combinatorial optimization problems, such as routing, scheduling, assignment, and mixed integer programming, including the use of neural network approximations within these contexts. The book focuses on the fundamental idea of policy iteration, i.e., start from some policy, and successively generate one or more improved policies. If just one improved policy is generated, this is called rollout, which, based on broad and consistent computational experience, appears to be one of the most versatile and reliable of all reinforcement learning methods. In this book, rollout algorithms are developed for both discrete deterministic and stochastic DP problems, and the development of distributed implementations in both multiagent and multiprocessor settings, aiming to take advantage of parallelism. Approximate policy iteration is more ambitious than rollout, but it is a strictly off-line method, and it is generally far more computationally intensive. This motivates the use of parallel and distributed computation. One of the purposes of the monograph is to discuss distributed (possibly asynchronous) methods that relate to rollout and policy iteration, both in the context of an exact and an approximate implementation involving neural networks or other approximation architectures. Much of the new research is inspired by the remarkable AlphaZero chess program, where policy iteration, value and policy networks, approximate lookahead minimization, and parallel computation all play an important role.

Multi-Agent Coordination

Author : Arup Kumar Sadhu
Publisher : John Wiley & Sons
ISBN 13 : 1119699037
Total Pages : 320 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Multi-Agent Coordination by : Arup Kumar Sadhu

Download or read book Multi-Agent Coordination written by Arup Kumar Sadhu and published by John Wiley & Sons. This book was released on 2020-12-03 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach 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. You'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. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach 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.

Readings in Agents

Author : Michael N. Huhns
Publisher : Morgan Kaufmann
ISBN 13 : 9781558604957
Total Pages : 552 pages
Book Rating : 4.6/5 (49 download)

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Book Synopsis Readings in Agents by : Michael N. Huhns

Download or read book Readings in Agents written by Michael N. Huhns and published by Morgan Kaufmann. This book was released on 1998 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects the most significant literature on agents in an attempt top forge a broad foundation for the field. Includes papers from the perspectives of AI, databases, distributed computing, and programming languages. The book will be of interest to programmers and developers, especially in Internet areas.

Multi-Agent Reinforcement Learning

Author : Stefano V. Albrecht
Publisher : MIT Press
ISBN 13 : 0262049376
Total Pages : 0 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Multi-Agent Reinforcement Learning by : Stefano V. Albrecht

Download or read book Multi-Agent Reinforcement Learning written by Stefano V. Albrecht and published by MIT Press. This book was released on 2024-12-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Innovations in Multi-Agent Systems and Application – 1

Author : Dipti Srinivasan
Publisher : Springer Science & Business Media
ISBN 13 : 3642144349
Total Pages : 303 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Innovations in Multi-Agent Systems and Application – 1 by : Dipti Srinivasan

Download or read book Innovations in Multi-Agent Systems and Application – 1 written by Dipti Srinivasan and published by Springer Science & Business Media. This book was released on 2010-08-10 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of multi-agent systems and several applications that have been developed for real-world problems. Multi-agent systems is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexities arising from their interactions. Multi-agent systems allow the subproblems of a constraint satisfaction problem to be subcontracted to different problem solving agents with their own interest and goals. This increases the speed, creates parallelism and reduces the risk of system collapse on a single point of failure. Different multi-agent architectures, that are tailor-made for a specific application are possible. They are able to synergistically combine the various computational intelligent techniques for attaining a superior performance. This gives an opportunity for bringing the advantages of various techniques into a single framework. It also provides the freedom to model the behavior of the system to be as competitive or coordinating, each having its own advantages and disadvantages.

Transfer Learning for Multiagent Reinforcement Learning Systems

Author : Felipe Felipe Leno da Silva
Publisher : Springer Nature
ISBN 13 : 3031015916
Total Pages : 111 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Transfer Learning for Multiagent Reinforcement Learning Systems by : Felipe Felipe Leno da Silva

Download or read book Transfer Learning for Multiagent Reinforcement Learning Systems written by Felipe Felipe Leno da Silva and published by Springer Nature. This book was released on 2022-06-01 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment. However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning. This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools. This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.

Handbook of Reinforcement Learning and Control

Author : Kyriakos G. Vamvoudakis
Publisher : Springer Nature
ISBN 13 : 3030609901
Total Pages : 833 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Handbook of Reinforcement Learning and Control by : Kyriakos G. Vamvoudakis

Download or read book Handbook of Reinforcement Learning and Control written by Kyriakos G. Vamvoudakis and published by Springer Nature. This book was released on 2021-06-23 with total page 833 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.

Adaptive Agents and Multi-Agent Systems II

Author : Daniel Kudenko
Publisher : Springer
ISBN 13 : 3540322744
Total Pages : 313 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Adaptive Agents and Multi-Agent Systems II by : Daniel Kudenko

Download or read book Adaptive Agents and Multi-Agent Systems II written by Daniel Kudenko and published by Springer. This book was released on 2005-02-18 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive agents and multi-agent systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, software engineering, and developmental biology, as well as cognitive and social science. This book presents 17 revised and carefully reviewed papers taken from two workshops on the topic as well as 2 invited papers by leading researchers in the area. The papers deal with various aspects of machine learning, adaptation, and evolution in the context of agent systems and autonomous agents.

Layered Learning in Multiagent Systems

Author : Peter Stone
Publisher : MIT Press
ISBN 13 : 9780262264600
Total Pages : 300 pages
Book Rating : 4.2/5 (646 download)

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Book Synopsis Layered Learning in Multiagent Systems by : Peter Stone

Download or read book Layered Learning in Multiagent Systems written by Peter Stone and published by MIT Press. This book was released on 2000-03-03 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. 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.

Reinforcement Learning

Author : Marco Wiering
Publisher : Springer Science & Business Media
ISBN 13 : 3642276458
Total Pages : 638 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Reinforcement Learning by : Marco Wiering

Download or read book Reinforcement Learning written by Marco Wiering and published by Springer Science & Business Media. This book was released on 2012-03-05 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research. Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge representation in reinforcement learning settings.

Adaptive Agents and Multi-Agent Systems

Author : Eduardo Alonso
Publisher : Springer Science & Business Media
ISBN 13 : 3540400680
Total Pages : 335 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Adaptive Agents and Multi-Agent Systems by : Eduardo Alonso

Download or read book Adaptive Agents and Multi-Agent Systems written by Eduardo Alonso and published by Springer Science & Business Media. This book was released on 2003-04-23 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive Agents and Multi-Agent Systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, computer science, software engineering, and developmental biology, as well as cognitive and social science. This book surveys the state of the art in this emerging field by drawing together thoroughly selected reviewed papers from two related workshops; as well as papers by leading researchers specifically solicited for this book. The articles are organized into topical sections on - learning, cooperation, and communication - emergence and evolution in multi-agent systems - theoretical foundations of adaptive agents

Reinforcement Learning

Author : Phil Winder Ph.D.
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492072346
Total Pages : 517 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Reinforcement Learning by : Phil Winder Ph.D.

Download or read book Reinforcement Learning written by Phil Winder Ph.D. and published by "O'Reilly Media, Inc.". This book was released on 2020-11-06 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website

An Introduction to MultiAgent Systems

Author : Michael Wooldridge
Publisher : John Wiley & Sons
ISBN 13 : 0470519460
Total Pages : 484 pages
Book Rating : 4.4/5 (75 download)

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Book Synopsis An Introduction to MultiAgent Systems by : Michael Wooldridge

Download or read book An Introduction to MultiAgent Systems written by Michael Wooldridge and published by John Wiley & Sons. This book was released on 2009-06-22 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The book’s main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology After reading the book the student should understand: The notion of an agent, how agents are distinct from other software paradigms (e.g. objects) and the characteristics of applications that lend themselves to agent-oriented software The key issues associated with constructing agents capable of intelligent autonomous action and the main approaches taken to developing such agents The key issues in designing societies of agents that can effectively cooperate in order to solve problems, including an understanding of the key types of multi-agent interactions possible in such systems The main application areas of agent-based systems

Recent Advances in Reinforcement Learning

Author : Scott Sanner
Publisher : Springer
ISBN 13 : 9783642299452
Total Pages : 0 pages
Book Rating : 4.2/5 (994 download)

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Book Synopsis Recent Advances in Reinforcement Learning by : Scott Sanner

Download or read book Recent Advances in Reinforcement Learning written by Scott Sanner and published by Springer. This book was released on 2012-05-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse reinforcement learning and real-world reinforcement learning.