Multiagent Learning in Non-stationary Environments PDF Download
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Author: Michael Weinberg
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
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Book Description
Author: Michael Weinberg
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
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Book Description
Author: Moamar Sayed-Mouchaweh
Publisher: Springer Science & Business Media
ISBN: 1441980202
Category : Technology & Engineering
Languages : en
Pages : 439
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Book Description
Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.
Author: Dong Ki Kim (Artificial intelligence expert)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Multiagent reinforcement learning (MARL) provides a principled framework for a group of artificial intelligence agents to learn collaborative and/or competitive behaviors at the level of human experts. Multiagent learning settings inherently solve much more complex problems than single-agent learning because an agent interacts both with the environment and other agents. In particular, multiple agents simultaneously learn in MARL, leading to natural non-stationarity in the experiences encountered and thus requiring each agent to its behavior with respect to potentially large changes in other agents' policies. This thesis aims to address the non-stationarity challenge in multiagent learning from three important topics: 1) adaptation, 2) convergence, and 3) state space. The first topic answers how an agent can learn effective adaptation strategies concerning other agents' changing policies by developing a new meta-learning framework. The second topic answers how agents can adapt and influence the joint learning process such that policies converge to more desirable limiting behaviors by the end of learning based on a new game-theoretical solution concept. Lastly, the last topic answers how state space size can be reduced based on knowledge sharing and context-specific abstraction such that the learning complexity is less affected by non-stationarity. In summary, this thesis develops theoretical and algorithmic contributions to provide principled answers to the aforementioned topics on non-stationarity. The developed algorithms in this thesis demonstrate their effectiveness in a diverse suite of multiagent benchmark domains, including the full spectrum of mixed incentive, competitive, and cooperative environments.
Author: Andrei Marinescu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Springer
Publisher:
ISBN: 9781441980212
Category :
Languages : en
Pages : 454
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Book Description
Author: Karl Tuyls
Publisher: Springer Science & Business Media
ISBN: 3540330534
Category : Computers
Languages : en
Pages : 225
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Book Description
This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Learning and Adaption in Multi-Agent Systems, LAMAS 2005, held in The Netherlands, in July 2005, as an associated event of AAMAS 2005. The 13 revised papers presented together with two invited talks were carefully reviewed and selected from the lectures given at the workshop.
Author: Gita Sukthankar
Publisher: Springer
ISBN: 3319716824
Category : Computers
Languages : en
Pages : 308
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Book Description
This book features a selection of best papers from 13 workshops held at the International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017, held in Sao Paulo, Brazil, in May 2017. The 17 full papers presented in this volume were carefully reviewed and selected for inclusion in this volume. They cover specific topics, both theoretical and applied, in the general area of autonomous agents and multiagent systems.
Author: Janmenjoy Nayak
Publisher: Springer Nature
ISBN: 3030719758
Category : Technology & Engineering
Languages : en
Pages : 461
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Book Description
This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.
Author: Karl Tuyls
Publisher: Springer Science & Business Media
ISBN: 3540779477
Category : Computers
Languages : en
Pages : 263
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Book Description
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.
Author: Arup Kumar Sadhu
Publisher: John Wiley & Sons
ISBN: 1119699029
Category : Computers
Languages : en
Pages : 320
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Book Description
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.
