Download or read book Machine Learning in Non stationary Environments written by Masashi Sugiyama and published by MIT Press. This book was released on 2012 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dealing with non-stationarity is one of modem machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity.

Download or read book Reinforcement Learning in Non stationary Environments written by Erwan Lecarpentier and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: How should an agent act in the face of uncertainty on the evolution of its environment?In this dissertation, we give a Reinforcement Learning perspective on the resolution of nonstationaryproblems. The question is seen from three different aspects. First, we study theplanning vs. re-planning trade-off of tree search algorithms in stationary Markov DecisionProcesses. We propose a method to lower the computational requirements of such an algorithmwhile keeping theoretical guarantees on the performance. Secondly, we study thecase of environments evolving gradually over time. This hypothesis is expressed through amathematical framework called Lipschitz Non-Stationary Markov Decision Processes. Wederive a risk averse planning algorithm provably converging to the minimax policy in thissetting. Thirdly, we consider abrupt temporal evolution in the setting of lifelong ReinforcementLearning. We propose a non-negative transfer method based on the theoretical study ofthe optimal Q-function's Lipschitz continuity with respect to the task space. The approachallows to accelerate learning in new tasks. Overall, this dissertation proposes answers to thequestion of solving Non-Stationary Markov Decision Processes under three different settings.

Download or read book Efficient Exploration of Reinforcement Learning in Non stationary Environments with More Complex State Dynamics written by Parker Ruochen Hao and published by . This book was released on 2020 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploration technique is the key to reach optimal results via reinforcement learning in a time-ecient manner. When reinforcement learning was first proposed, exploration was implemented as randomly choosing across the action space, resulting in potentially exponential number of state-action pairs to explore from. Over the years, more ecient exploration techniques were proposed, allowing faster convergence and delivering better results across different domains of applications. With the growing interest in non-stationary environments, some of those exploration techniques are explored where the optimal state-action changes across dierent periods of learning process. In the past, those techniques have performed well in control setups where the targets are non-stationary and continuously moving. However, such techniques have not been extensively tested in environments involving jumps or non-continuous regime changes. This paper analyzes methods for achieving comparable exploration performance under such challenging environments and proposes new techniques for the agent to capture the regime changes of non-stationary environments as more complex states or intrinsic rewards.

Download or read book Machine Learning in Non Stationary Environments written by Masashi Sugiyama and published by MIT Press. This book was released on 2012-03-30 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

Download or read book Machine Learning in Non Stationary Environments written by Motoaki Kawanabe and published by . This book was released on with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity.

Download or read book Adapting Machine Learning to Non stationary Environments written by Wintheiser Donnie and published by . This book was released on 2023-04-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning stimulates a broad range of computational methods that exploit experience, which typically takes the form of electronic data, to make profitable decisions or accurate predictions. To date, the machine learning models have been applied to extensive application domains across diverse fields, including but not limited to computer vision [1, 2, 3], natural language processing [4, 5, 6], robotic control [7, 8], and cyber security [9, 10, 11].

Download or read book Reinforcement Learning in Episodic Non stationary Markovian Environments written by Samuel P. M. Choi and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Prediction based Multi agent Reinforcement Learning for Inherently Non stationary Environments written by Andrei Marinescu and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Making the World Differentiable written by Jürgen H. Schmidhuber and published by . This book was released on 1990 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Non Stationary Reinforcement Learning written by Wang Chi Cheung and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated by applications in inventory control and real-time bidding, we consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under temporal drifts. In this setting, both the reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain emph{variation budgets}. We first develop the Sliding Window Upper-Confidence bound for Reinforcement Learning with Confidence Widening ( texttt{SWUCRL2-CW}) algorithm, and establish its emph{dynamic regret} bound when the variation budgets are known. In addition, we propose the Bandit-over-Reinforcement Learning ( texttt{BORL}) algorithm to adaptively tune the sw~to achieve the same dynamic regret bound, but in a emph{parameter-free} manner, ie, without knowing the variation budgets. Finally, we conduct numerical experiments to show that our proposed algorithms achieve superior empirical performance compared with existing algorithms. Notably, under non-stationarity, historical data samples may falsely indicate that state transition rarely happens. This thus presents a significant challenge when one tries to apply the conventional Optimism in the Face of Uncertainty (OFU) principle to achieve low dynamic regret bound our problem. We overcome this challenge by proposing a novel confidence widening technique that incorporates additional optimism into our learning algorithms. To extend our theoretical findings, we demonstrate, in the context of single item inventory control with lost-sales, fixed cost, and zero-lead time, how one can leverage special structures on the state transition distributions to bypass the difficulty of exploring time-varying demand environments.

Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Download or read book Reinforcement Learning in Non stationary Games written by Omid Namvar Gharehshiran and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Effective Learning in Non stationary Multiagent Environments written by Dong Ki Kim (Artificial intelligence expert) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Special Topics in Information Technology written by Luigi Piroddi and published by Springer Nature. This book was released on 2022-01-01 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists.

Download or read book Markov Decision Processes written by Martin L. Puterman and published by John Wiley & Sons. This book was released on 2014-08-28 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential." —Zentralblatt fur Mathematik ". . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes." —Journal of the American Statistical Association

Download or read book Metaheuristics written by Mauricio G.C. Resende and published by Springer Science & Business Media. This book was released on 2003-11-30 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combinatorial optimization is the process of finding the best, or optimal, so lution for problems with a discrete set of feasible solutions. Applications arise in numerous settings involving operations management and logistics, such as routing, scheduling, packing, inventory and production management, lo cation, logic, and assignment of resources. The economic impact of combi natorial optimization is profound, affecting sectors as diverse as transporta tion (airlines, trucking, rail, and shipping), forestry, manufacturing, logistics, aerospace, energy (electrical power, petroleum, and natural gas), telecommu nications, biotechnology, financial services, and agriculture. While much progress has been made in finding exact (provably optimal) so lutions to some combinatorial optimization problems, using techniques such as dynamic programming, cutting planes, and branch and cut methods, many hard combinatorial problems are still not solved exactly and require good heuristic methods. Moreover, reaching "optimal solutions" is in many cases meaningless, as in practice we are often dealing with models that are rough simplifications of reality. The aim of heuristic methods for combinatorial op timization is to quickly produce good-quality solutions, without necessarily providing any guarantee of solution quality. Metaheuristics are high level procedures that coordinate simple heuristics, such as local search, to find solu tions that are of better quality than those found by the simple heuristics alone: Modem metaheuristics include simulated annealing, genetic algorithms, tabu search, GRASP, scatter search, ant colony optimization, variable neighborhood search, and their hybrids.

Download or read book Statistical Reinforcement Learning written by Masashi Sugiyama and published by CRC Press. This book was released on 2015-03-16 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and gaming have been successfully explored in recent years. Providing an accessible introduction to the field, this book covers model-based and model-free approaches, policy iteration, and policy search methods. It presents illustrative examples and state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. The book provides a bridge between RL and data mining and machine learning research.