Download or read book Model based Reinforcement Learning for Cooperative Multi agent Planning written by Aaron Ma and published by . This book was released on 2020 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous unmanned vehicles (UxVs) can be useful in many scenarios including disaster relief, production and manufacturing, as well as carrying out Naval missions such as surveillance, mapping of unknown regions and pursuit of other hostile vehicles. When considering these scenarios, one of the most difficult challenges is determining which actions or tasks the vehicles should take in order to most efficiently satisfy the objectives. This challenge becomes more difficult with the inclusion of multiple vehicles, because the action and state space scale exponentially with the number of agents. Many planning algorithms suffer from the curse of dimensionality as more agents are included, sampling for suitable actions in the joint action space becomes infeasible within a reasonable amount of time. To enable autonomy, methods that can be applied to a variety of scenarios are invaluable because they reduce human involvement and time. Recently, advances in technology enable algorithms that require more computational power to be effective but work in broader frameworks. We offer three main approaches to multi-agent planning which are all inspired by model-based reinforcement learning. First, we address the curse of dimensionality and investigate how to spatially reduce the state space of massive environments where agents are deployed. We do this in a hierarchical fashion by searching subspaces of the environment, called sub-environments, and creating plans to optimally take actions in those sub-environments. Next, we utilize game-theoretic techniques paired with simulated annealing as an approach for agent cooperation when planning in a finite time horizon. One problem with this approach is that agents are capable of breaking promises with other agents right before execution. To address this, we propose several variations that discourage agents from changing plans in the near future and encourages joint planning in the long term. Lastly, we propose a tree-search algorithm that is aided by a convolutional neural network. The convolutional neural network takes advantage of spatial features that are natural in UxV deployment and offers recommendations for action selection during tree search. In addition, we propose some design features for the tree search that target multi-agent deployment applications.

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.

Download or read book Advanced Machine Learning Approaches in Cancer Prognosis written by Janmenjoy Nayak and published by Springer Nature. This book was released on 2021-05-29 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Learning in Cooperative Multi Agent Systems written by Thomas Gabel and published by Sudwestdeutscher Verlag Fur Hochschulschriften AG. This book was released on 2009-09 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a distributed system, a number of individually acting agents coexist. In order to achieve a common goal, coordinated cooperation between the agents is crucial. Many real-world applications are well-suited to be formulated in terms of spatially or functionally distributed entities. Job-shop scheduling represents one such application. Multi-agent reinforcement learning (RL) methods allow for automatically acquiring cooperative policies based solely on a specification of the desired joint behavior of the whole system. However, the decentralization of the control and observation of the system among independent agents has a significant impact on problem complexity. The author Thomas Gabel addresses the intricacy of learning and acting in multi-agent systems by two complementary approaches. He identifies a subclass of general decentralized decision-making problems that features provably reduced complexity. Moreover, he presents various novel model-free multi-agent RL algorithms that are capable of quickly obtaining approximate solutions in the vicinity of the optimum. All algorithms proposed are evaluated in the scope of various established scheduling benchmark problems.

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.

Download or read book Model based Reinforcement Learning written by Thomas M. Moerland and published by . This book was released on 2023-01-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This monograph surveys an integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps: dynamics model learning and planning-learning integration. In this comprehensive survey of the topic, the authors first cover dynamics model learning, including challenges such as dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. They then present a systematic categorization of planning-learning integration, including aspects such as: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. In conclusion the authors discuss implicit model-based RL as an end-to-end alternative for model learning and planning, and cover the potential benefits of model-based RL. Along the way, the authors draw connections to several related RL fields, including hierarchical RL and transfer learning. This monograph contains a broad conceptual overview of the combination of planning and learning for Markov Decision Process optimization. It provides a clear and complete introduction to the topic for students and researchers alike.

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.

Download or read book Transfer Learning for Multiagent Reinforcement Learning Systems written by Felipe Leno da Silva and published by Morgan & Claypool Publishers. This book was released on 2021-05-27 with total page 131 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.

Download or read book Structured Cooperative Multi agent Coordination written by Sheng Li and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) involves solving decision-making tasks by learning from the experience of agents without prior knowledge of the dynamics of the problems. The advances in deep neural networks and reinforcement learning have seen success in several application domains including game playing, autonomous driving, and finance. In many real-world problems, multiple entities make decisions and interact with each other within a shared environment. It is hard to model the decision-maker as a single agent in such problems. Instead, it is more natural and reasonable to model each participating entity as an agent and frame the problem as multi-agent reinforcement learning (MARL). MARL is more challenging to solve than single-agent RL due to the complexity of inter- agent interaction and limited information for each agent. The major challenges in MARL are: 1. scalability, the complexity of MARL problems grows with the number of agents; 2. partial observability, each agent lacks sufficient information to coordinate optimally due to limited local observation; 3. non-stationarity, each agent sees other agents as a part of the environment, whose evolving behavioral patterns create non-stationary environment dynamics, breaking the Markov property assumption of RL algorithms. This dissertation explores approaches to address the aforementioned challenges. We exploit the structural nature of inter-agent interaction to achieve effective coordination between agents. We use techniques from reinforcement learning and deep learning to develop efficient MARL algorithms. In the first contribution, we demonstrate solving a multi-agent problem from a single agent's perspective using utility decomposition and fusion in an approximate and decentralized way that relies on the spatial structure between agents. On top of it, we use deep Q-learning to optimize the approximate solution. In the second contribution, we use a graph structure to model the interaction between agents and use the attention mechanism to dynamically learn the graph representation. We further conduct communication of the graph to achieve multi-agent coordination. Finally, in the last contribution, we explore and analyze learning emergent discrete message communication and its interpretability. We demonstrate an explicit broadcast-based communication model and study a human-agent interaction method using discrete message communication. In summary, in this dissertation, we try to find a balanced mid-ground between the optimality and the efficiency in solving MARL problems.

Download or read book Machine Learning and Knowledge Discovery in Databases Research Track written by Danai Koutra and published by Springer Nature. This book was released on 2023-09-17 with total page 789 pages. Available in PDF, EPUB and Kindle. Book excerpt: The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

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 653 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.

Download or read book Multi model Based Reinforcement Learning and Application written by Haoran Wei and published by . This book was released on 2020 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: We first propose using a multi-agent system with dispersed exploration foci to explore a shared environment simultaneously and accelerate a single environment interactive agent's RL. A high-fidelity environment simulator is assumed to be avail- able. A single agent's global RL policy is aggregated from distributed local policies.

Download or read book Advances in Reinforcement Learning written by Abdelhamid Mellouk and published by BoD – Books on Demand. This book was released on 2011-01-14 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic.

Download or read book A Concise Introduction to Decentralized POMDPs written by Frans A. Oliehoek and published by Springer. This book was released on 2016-06-03 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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: The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches. Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the field’s foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers. First textbook to introduce the foundations and applications of MARL, written by experts in the field Integrates reinforcement learning, deep learning, and game theory Practical focus covers considerations for running experiments and describes environments for testing MARL algorithms Explains complex concepts in clear and simple language Classroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics Resources include code and slides

Download or read book Proceedings of the 2nd International Conference on Internet of Things Communication and Intelligent Technology written by Jian Dong and published by Springer Nature. This book was released on with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi Objective Decision Making written by Diederik M. Zhou and published by Springer Nature. This book was released on 2022-05-31 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.