Download or read book Selectively Decentralized Reinforcement Learning written by Thanh Minh Nguyen and published by . This book was released on 2018 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main contributions in this thesis include the selectively decentralized method in solving multi-agent reinforcement learning problems and the discretized Markov-decision-process (MDP) algorithm to compute the sub-optimal learning policy in completely unknown learning and control problems. These contributions tackle several challenges in multi-agent reinforcement learning: the unknown and dynamic nature of the learning environment, the difficulty in computing the closed-form solution of the learning problem, the slow learning performance in large-scale systems, and the questions of how/when/to whom the learning agents should communicate among themselves. Through this thesis, the selectively decentralized method, which evaluates all of the possible communicative strategies, not only increases the learning speed, achieves better learning goals but also could learn the communicative policy for each learning agent. Compared to the other state-of-the-art approaches, this thesis's contributions offer two advantages. First, the selectively decentralized method could incorporate a wide range of well-known algorithms, including the discretized MDP, in single-agent reinforcement learning; meanwhile, the state-of-the-art approaches usually could be applied for one class of algorithms. Second, the discretized MDP algorithm could compute the sub-optimal learning policy when the environment is described in general nonlinear format; meanwhile, the other state-of-the-art approaches often assume that the environment is in limited format, particularly in feedback-linearization form. This thesis also discusses several alternative approaches for multi-agent learning, including Multidisciplinary Optimization. In addition, this thesis shows how the selectively decentralized method could successfully solve several real-worlds problems, particularly in mechanical and biological systems.

Download or read book Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems written by Jim Dowling and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems.

Download or read book Cooperative Multi agent Reinforcement Learning in Decentralized Networks written by Martin Figura and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Decentralized and Partially Decentralized Multi agent Reinforcement Learning written by Omkar Jayant Tilak and published by . This book was released on 2012 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-agent systems consist of multiple agents that interact and coordinate with each other to work towards to certain goal. Multi-agent systems naturally arise in a variety of domains such as robotics, telecommunications, and economics. The dynamic and complex nature of these systems entails the agents to learn the optimal solutions on their own instead of following a pre-programmed strategy. Reinforcement learning provides a framework in which agents learn optimal behavior based on the response obtained from the environment. In this thesis, we propose various novel de- centralized, learning automaton based algorithms which can be employed by a group of interacting learning automata. We propose a completely decentralized version of the estimator algorithm. As compared to the completely centralized versions proposed before, this completely decentralized version proves to be a great improvement in terms of space complexity and convergence speed. The decentralized learning algorithm was applied; for the first time; to the domains of distributed object tracking and distributed watershed management. The results obtained by these experiments show the usefulness of the decentralized estimator algorithms to solve complex optimization problems. Taking inspiration from the completely decentralized learning algorithm, we propose the novel concept of partial decentralization. The partial decentralization bridges the gap between the completely decentralized and completely centralized algorithms and thus forms a comprehensive and continuous spectrum of multi-agent algorithms for the learning automata. To demonstrate the applicability of the partial decentralization, we employ a partially decentralized team of learning automata to control multi-agent Markov chains. More flexibility, expressiveness and flavor can be added to the partially decentralized framework by allowing different decentralized modules to engage in different types of games. We propose the novel framework of heterogeneous games of learning automata which allows the learning automata to engage in disparate games under the same formalism. We propose an algorithm to control the dynamic zero-sum games using heterogeneous games of learning automata.

Download or read book Reinforcement Learning written by Cornelius Weber and published by IntechOpen. This book was released on 2008-01-01 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field.

Download or read book Decentralized Identity Explained written by Rohan Pinto and published by Packt Publishing Ltd. This book was released on 2024-07-19 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delve into the cutting-edge trends of decentralized identities, blockchains, and other digital identity management technologies and leverage them to craft seamless digital experiences for both your customers and employees Key Features Explore decentralized identities and blockchain technology in depth Gain practical insights for leveraging advanced digital identity management tools, frameworks, and solutions Discover best practices for integrating decentralized identity solutions into existing systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionLooking forward to mastering digital identity? This book will help you get to grips with complete frameworks, tools, and strategies for safeguarding personal data, securing online transactions, and ensuring trust in digital interactions in today's cybersecurity landscape. Decentralized Identity Explained delves into the evolution of digital identities, from their historical roots to the present landscape and future trajectories, exploring crucial concepts such as IAM, the significance of trust anchors and sources of truth, and emerging trends such as SSI and DIDs. Additionally, you’ll gain insights into the intricate relationships between trust and risk, the importance of informed consent, and the evolving role of biometrics in enhancing security within distributed identity management systems. Through detailed discussions on protocols, standards, and authentication mechanisms, this book equips you with the knowledge and tools needed to navigate the complexities of digital identity management in both current and future cybersecurity landscapes. By the end of this book, you’ll have a detailed understanding of digital identity management and best practices to implement secure and efficient digital identity frameworks, enhancing both organizational security and user experiences in the digital realm.What you will learn Understand the need for security, privacy, and user-centric methods Get up to speed with the IAM security framework Explore the crucial role of sources of truth in identity data verification Discover best practices for implementing access control lists Gain insights into the fundamentals of informed consent Delve into SSI and understand why it matters Explore identity verification methods such as knowledge-based and biometric Who this book is for This book is for cybersecurity professionals and IAM engineers/architects who want to learn how decentralized identity helps to improve security and privacy and how to leverage it as a trust framework for identity management.

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.

Download or read book Deep Reinforcement Learning in Action written by Alexander Zai and published by Manning Publications. This book was released on 2020-04-28 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap

Download or read book Reinforcement Learning written by Cornelius Weber and published by BoD – Books on Demand. This book was released on 2008-01-01 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field.

Download or read book The Art of Reinforcement Learning written by Michael Hu and published by Apress. This book was released on 2023-08-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology. Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO). This book also delves into advanced topics, including distributed reinforcement learning, curiosity-driven exploration, and the famous AlphaZero algorithm, providing readers with a detailed account of these cutting-edge techniques. With a focus on explaining algorithms and the intuition behind them, The Art of Reinforcement Learning includes practical source code examples that you can use to implement RL algorithms. Upon completing this book, you will have a deep understanding of the concepts, mathematics, and algorithms behind reinforcement learning, making it an essential resource for AI practitioners, researchers, and students. What You Will Learn Grasp fundamental concepts and distinguishing features of reinforcement learning, including how it differs from other AI and non-interactive machine learning approaches Model problems as Markov decision processes, and how to evaluate and optimize policies using dynamic programming, Monte Carlo methods, and temporal difference learning Utilize techniques for approximating value functions and policies, including linear and nonlinear value function approximation and policy gradient methods Understand the architecture and advantages of distributed reinforcement learning Master the concept of curiosity-driven exploration and how it can be leveraged to improve reinforcement learning agents Explore the AlphaZero algorithm and how it was able to beat professional Go players Who This Book Is For Machine learning engineers, data scientists, software engineers, and developers who want to incorporate reinforcement learning algorithms into their projects and applications.

Download or read book TensorFlow Reinforcement Learning Quick Start Guide written by Kaushik Balakrishnan and published by Packt Publishing Ltd. This book was released on 2019-03-30 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.

Download or read book Multi Agent Reinforcement Learning and Adaptive Neural Networks written by and published by . This book was released on 1996 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project investigated learning systems consisting of multiple interacting controllers, or agents: each of which employed a modern reinforcement learning method. The objective was to study the utility of reinforcement learning as an approach to complex decentralized control problems. The major accomplishment was a detailed study of multi-agent reinforcement learning applied to a large-scale decentralized stochastic control problem. This study included a very successful demonstration that a multi-agent reinforcement learning system using neural networks could learn high-performance dispatching of multiple elevator cars in a simulated multi-story building. This problem is representative of very large-scale dynamic optimization problems of practical importance that are intractable for standard methods. The performance achieved by the distributed elevator controller surpassed that of the best of the elevator control algorithms accessible in the literature, showing that reinforcement learning can be a useful approach to difficult decentralized control problems. Additional empirical results demonstrated the performance of reinforcement learning-systems in the setting of nonzero-sum games, with mixed results. Some progress was also made in improving theoretical understanding of multi-agent reinforcement learning.

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 Distributional Reinforcement Learning written by Marc G. Bellemare and published by MIT Press. This book was released on 2023-05-30 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment. The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions.

Download or read book Interference Mitigation with Selective Retransmissions in Wireless Sensor Networks written by Selig, Marc and published by kassel university press GmbH. This book was released on 2016 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: A wireless sensor network with single-antenna sensors on the transmitter side and an access point (AP) equipped with multiple antennas on the receiver side is considered. In order to reduce the number of outages resulting from the noise amplification by the linear reconstruction within the successive interference cancellation (SIC) procedure, the AP is given the possibility to request retransmissions of signals from selected sensors in a subsequent time slot (TS). In case retransmissions are needed also in the subsequent time slot, the AP postpones the signal detection until all requested signals have been retransmitted making the signal detection a recursive procedure. The number of sensors required to retransmit depends on the order of the processed sensor signals within the SIC procedure. We propose an optimal algorithm based on a QR-decomposition and a depth-first search through all possible decoding orders, which finds the decoding order necessitating a minimum number of retransmissions suitable for zero-forcing (ZF) and minimum mean square error (MMSE) linear reconstruction approaches. Since the computational complexity of the optimal algorithm is high, different suboptimal algorithms with lower computational complexity are proposed for the case of ZFSIC and MMSE-SIC, respectively. The recursive nature of the retransmission procedure may lead to an unlimited detection delay, because the linear reconstruction followed by SIC starts only when no sensor needs a retransmission from the previous TS. By reducing the number of transmitting sensors for a fixed number of receiving antennas the receive diversity of the AP can be exploited, which leads to less retransmissions. Therefore, we propose an optimal transmit policy, which selects the best set of sensors to maximize the system throughput. This optimal transmit policy is found by means of a Markov decision process in combination with dynamic programming.

Download or read book Green Blockchain Technology for Sustainable Smart Cities written by Saravanan Krishnan and published by Elsevier. This book was released on 2023-05-05 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Green Blockchain Technology for Sustainable Smart Cities presents a detailed exploration of the adaptation and implementation of green blockchain technology for sustainable and eco-friendly smart city applications. This book covers all aspects of the topic and explores smart cities ecosystem applications of blockchain technology. Novel architectural and business blockchain use case solutions in smart city implementations are at the core of this book, which will be beneficial for all researchers, engineers, graduate students, smart city practitioners, and city administrators who are engaged in green blockchain and smart cities-related technologies. Covers a wide variety of topics Offers readers multiple perspectives from a variety of disciplines Written by an internationally diverse group of experts in their respective fields Includes a section on use cases as well as current challenges and future directions