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Decentralised Reinforcement Learning In Markov Games

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Decentralised Reinforcement Learning In Markov Games

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Author : Peter Vrancx
language : en
Publisher: ASP / VUBPRESS / UPA
Release Date : 2011

Decentralised Reinforcement Learning In Markov Games written by Peter Vrancx and has been published by ASP / VUBPRESS / UPA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.

Introducing a new approach to multiagent reinforcement learning and distributed artificial intelligence, this guide shows how classical game theory can be used to compose basic learning units. This approach to creating agents has the advantage of leading to powerful, yet intuitively simple, algorithms that can be analyzed. The setup is demonstrated here in a number of different settings, with a detailed analysis of agent learning behaviors provided for each. A review of required background materials from game theory and reinforcement learning is also provided, along with an overview of related multiagent learning methods.

Reinforcement Learning

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Author : Marco Wiering
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-03-05

Reinforcement Learning written by Marco Wiering and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-05 with Computers categories.

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.

Multiagent System Technologies

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Author : Jan Ole Berndt
language : en
Publisher: Springer
Release Date : 2017-08-11

Multiagent System Technologies written by Jan Ole Berndt and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-11 with Computers categories.

This book constitutes the proceedings of the 15th German Conference on Multiagent System Technologies, MATES 2017, held in Lepzig, Germany, in August 2017. The 17 full papers presented in this volume were carefully reviewed and selected from 24 submissions for inclusion in the proceedings. Over these 15 years, the MATES conference series has been aiming at the promotion of and the cross-fertilization between theory and application of intelligent agents and multi-agent systems.

A Concise Introduction To Decentralized Pomdps

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Author : Frans A. Oliehoek
language : en
Publisher: Springer
Release Date : 2016-06-14

A Concise Introduction To Decentralized Pomdps written by Frans A. Oliehoek and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-14 with Computers categories.

This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.

Markov Decision Processes In Artificial Intelligence

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Author : Olivier Sigaud
language : en
Publisher: John Wiley & Sons
Release Date : 2013-03-04

Markov Decision Processes In Artificial Intelligence written by Olivier Sigaud and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-04 with Technology & Engineering categories.

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.

Handbook Of Reinforcement Learning And Control

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Author : Kyriakos G. Vamvoudakis
language : en
Publisher: Springer Nature
Release Date : 2021-06-23

Handbook Of Reinforcement Learning And Control written by Kyriakos G. Vamvoudakis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-23 with Technology & Engineering categories.

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.

Reinforcement Learning

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Author : Richard S. Sutton
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Reinforcement Learning written by Richard S. Sutton and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.

Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.

A Concise Introduction To Decentralized Pomdps

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Author : Frans A. Oliehoek
language : en
Publisher: Springer
Release Date : 2016-06-03

Adaptive Agents And Multi Agent Systems Iii Adaptation And Multi Agent Learning

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Author : Karl Tuyls
language : en
Publisher: Springer
Release Date : 2008-02-09

Adaptive Agents And Multi Agent Systems Iii Adaptation And Multi Agent Learning written by Karl Tuyls and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-09 with Computers categories.

This book contains selected and revised papers of the European Symposium on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS), editions 2005, 2006 and 2007, held in Paris, Brussels and Maastricht. The goal of the ALAMAS symposia, and this associated book, is to increase awareness and interest in adaptation and learning for single agents and mul- agent systems, and encourage collaboration between machine learning experts, softwareengineeringexperts,mathematicians,biologistsandphysicists,andgive a representative overviewof current state of a?airs in this area. It is an inclusive forum where researchers can present recent work and discuss their newest ideas for a ?rst time with their peers. Thesymposiaseriesfocusesonallaspectsofadaptiveandlearningagentsand multi-agent systems, with a particular emphasis on how to modify established learning techniques and/or create new learning paradigms to address the many challenges presented by complex real-world problems. These symposia were a great success and provided a forum for the pres- tation of new ideas and results bearing on the conception of adaptation and learning for single agents and multi-agent systems. Over these three editions we received 51 submissions, of which 17 were carefully selected, including one invited paper of this year’s invited speaker Simon Parsons. This is a very c- petitive acceptance rate of approximately 31%, which, together with two review cycles, has led to a high-quality LNAI volume. We hope that our readers will be inspired by the papers included in this volume.

Competitive Markov Decision Processes

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Author : Jerzy Filar
language : en
Publisher: Springer
Release Date : 1996-11-15

Competitive Markov Decision Processes written by Jerzy Filar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-11-15 with Mathematics categories.

This book is intended as a text covering the central concepts and techniques of Competitive Markov Decision Processes. It is an attempt to present a rig orous treatment that combines two significant research topics: Stochastic Games and Markov Decision Processes, which have been studied exten sively, and at times quite independently, by mathematicians, operations researchers, engineers, and economists. Since Markov decision processes can be viewed as a special noncompeti tive case of stochastic games, we introduce the new terminology Competi tive Markov Decision Processes that emphasizes the importance of the link between these two topics and of the properties of the underlying Markov processes. The book is designed to be used either in a classroom or for self-study by a mathematically mature reader. In the Introduction (Chapter 1) we outline a number of advanced undergraduate and graduate courses for which this book could usefully serve as a text. A characteristic feature of competitive Markov decision processes - and one that inspired our long-standing interest - is that they can serve as an "orchestra" containing the "instruments" of much of modern applied (and at times even pure) mathematics. They constitute a topic where the instruments of linear algebra, applied probability, mathematical program ming, analysis, and even algebraic geometry can be "played" sometimes solo and sometimes in harmony to produce either beautifully simple or equally beautiful, but baroque, melodies, that is, theorems.

Decentralised Reinforcement Learning In Markov Games

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