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Importance Sampling For Reinforcement Learning With Multiple Objectives
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Book Synopsis Importance Sampling for Reinforcement Learning with Multiple Objectives by : Christian Robert Shelton
Download or read book Importance Sampling for Reinforcement Learning with Multiple Objectives written by Christian Robert Shelton and published by . This book was released on 2001 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis considers three complications that arise from applying reinforcement learning to a real-world application. In the process of using reinforcement learning to build an adaptive electronic market-maker, we find the sparsity of data, the partial observability of the domain, and the multiple objectives of the agent to cause serious problems for existing reinforcement learning algorithms. We employ importance sampling (likelihood ratios) to achieve good performance in partially observable Nlarkov decision processes with few data. Our importance sampling estimator requires no knowledge about the environment and places few restrictions on the method of collecting data. It can be used efficiently with reactive controllers, finite-state controllers, or policies with function approximation. We present theoretical analyses of the estimator and incorporate it into a reinforcement learning algorithm. Additionally, this method provides a complete return surface which can be used to balance multiple objectives dynamically. We demonstrate the need for multiple goals in a variety of applications and natural solutions based on our sampling method. The thesis concludes with example results from employing our algorithm to the domain of automated electronic market-making.
Book Synopsis Cognitive Systems and Signal Processing by : Fuchun Sun
Download or read book Cognitive Systems and Signal Processing written by Fuchun Sun and published by Springer Nature. This book was released on 2021-05-04 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020, held in Zhuhai, China, in December 2020. The 59 revised papers presented were carefully reviewed and selected from 120 submissions. The papers are organized in topical sections on algorithm; application; manipulation; bioinformatics; vision; and autonomous vehicles.
Book Synopsis Reinforcement Learning, second edition by : Richard S. Sutton
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.
Book Synopsis Markov Decision Processes in Artificial Intelligence by : Olivier Sigaud
Download or read book Markov Decision Processes in Artificial Intelligence written by Olivier Sigaud and published by John Wiley & Sons. This book was released on 2013-03-04 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis AI 2008: Advances in Artificial Intelligence by : Wayne Wobcke
Download or read book AI 2008: Advances in Artificial Intelligence written by Wayne Wobcke and published by Springer Science & Business Media. This book was released on 2008-11-13 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 21th Australasian Joint Conference on Artificial Intelligence, AI 2008, held in Auckland, New Zealand, in December 2008. The 42 revised full papers and 21 revised short papers presented together with 1 invited lecture were carefully reviewed and selected from 143 submissions. The papers are organized in topical sections on knowledge representation, constraints, planning, grammar and language processing, statistical learning, machine learning, data mining, knowledge discovery, soft computing, vision and image processing, and AI applications.
Book Synopsis Applications of Computational Intelligence in Data-Driven Trading by : Cris Doloc
Download or read book Applications of Computational Intelligence in Data-Driven Trading written by Cris Doloc and published by John Wiley & Sons. This book was released on 2019-10-31 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.
Book Synopsis AI 2009: Advances in Artificial Intelligence by : Ann Nicholson
Download or read book AI 2009: Advances in Artificial Intelligence written by Ann Nicholson and published by Springer Science & Business Media. This book was released on 2009-11-09 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are pleased to present this LNCS volume, the Proceedings of the 22nd A- tralasianJointConferenceonArti?cialIntelligence(AI2009),heldinMelbourne, Australia, December 1–4,2009.This long established annual regionalconference is a forum both for the presentation of researchadvances in arti?cial intelligence and for scienti?c interchange amongst researchers and practitioners in the ?eld of arti?cial intelligence. Conference attendees were also able to enjoy AI 2009 being co-located with the Australasian Data Mining Conference (AusDM 2009) and the 4th Australian Conference on Arti?cial Life (ACAL 2009). This year AI 2009 received 174 submissions, from authors of 30 di?erent countries. After an extensive peer review process where each submitted paper was rigorously reviewed by at least 2 (and in most cases 3) independent revi- ers, the best 68 papers were selected by the senior Program Committee for oral presentation at the conference and included in this volume, resulting in an - ceptance rate of 39%. The papers included in this volume cover a wide range of topics in arti?cial intelligence: from machine learning to natural language s- tems, from knowledge representation to soft computing, from theoretical issues to real-world applications. AI 2009 also included 11 tutorials, available through the First Australian Computational Intelligence Summer School (ACISS 2009). These tutorials – some introductory, some advanced – covered a wide range of research topics within arti?cial intelligence, including data mining, games, evolutionary c- putation, swarm optimization, intelligent agents, Bayesian and belief networks.
Book Synopsis AI 2015: Advances in Artificial Intelligence by : Bernhard Pfahringer
Download or read book AI 2015: Advances in Artificial Intelligence written by Bernhard Pfahringer and published by Springer. This book was released on 2015-11-21 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 28th Australasian Joint Conference on Artificial Intelligence, AI 2015, held in Canberra, Australia, in November/December 2015. The 39 full papers and 18 short papers presented were carefully reviewed and selected from 102 submissions.
Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Annalisa Appice
Download or read book Machine Learning and Knowledge Discovery in Databases written by Annalisa Appice and published by Springer. This book was released on 2015-08-28 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
Book Synopsis Runtime Verification by : Bernd Finkbeiner
Download or read book Runtime Verification written by Bernd Finkbeiner and published by Springer Nature. This book was released on 2019-10-03 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Runtime Verification, RV 2019, held in Porto, Portugal, in October 2019. The 25 regular papers presented in this book were carefully reviewed and selected from 38 submissions. The RV conference is concerned with all aspects of monitoring and analysis of hardware, software and more general system executions. Runtime verification techniques are lightweight techniques to assess system correctness, reliability, and robustness; these techniques are significantly more powerful and versatile than conventional testing, and more practical than exhaustive formal verification. Chapter “Assumption-Based Runtime Verification with Partial Observability and Resets” and chapter “NuRV: a nuXmv Extension for Runtime Verification“ are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Book Synopsis Data Efficient Reinforcement Learning with Off-policy and Simulated Data by : Josiah Paul Hanna
Download or read book Data Efficient Reinforcement Learning with Off-policy and Simulated Data written by Josiah Paul Hanna and published by . This book was released on 2019 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning from interaction with the environment -- trying untested actions, observing successes and failures, and tying effects back to causes -- is one of the first capabilities we think of when considering autonomous agents. Reinforcement learning (RL) is the area of artificial intelligence research that has the goal of allowing autonomous agents to learn in this way. Despite much recent success, many modern reinforcement learning algorithms are still limited by the requirement of large amounts of experience before useful skills are learned. Two possible approaches to improving data efficiency are to allow algorithms to make better use of past experience collected with past behaviors (known as off-policy data) and to allow algorithms to make better use of simulated data sources. This dissertation investigates the use of such auxiliary data by answering the question, "How can a reinforcement learning agent leverage off-policy and simulated data to evaluate and improve upon the expected performance of a policy?" This dissertation first considers how to directly use off-policy data in reinforcement learning through importance sampling. When used in reinforcement learning, importance sampling is limited by high variance that leads to inaccurate estimates. This dissertation addresses this limitation in two ways. First, this dissertation introduces the behavior policy gradient algorithm that adapts the data collection policy towards a policy that generates data that leads to low variance importance sampling evaluation of a fixed policy. Second, this dissertation introduces the family of regression importance sampling estimators which improve the weighting of already collected off-policy data so as to lower the variance of importance sampling evaluation of a fixed policy. In addition to evaluation of a fixed policy, we apply the behavior policy gradient algorithm and regression importance sampling to batch policy gradient policy improvement. In the case of regression importance sampling, this application leads to the introduction of the sampling error corrected policy gradient estimator that improves the data efficiency of batch policy gradient algorithms. Towards the goal of learning from simulated experience, this dissertation introduces an algorithm -- the grounded action transformation algorithm -- that takes small amounts of real world data and modifies the simulator such that skills learned in simulation are more likely to carry over to the real world. Key to this approach is the idea of local simulator modification -- the simulator is automatically altered to better model the real world for actions the data collection policy would take in states the data collection policy would visit. Local modification necessitates an iterative approach: the simulator is modified, the policy improved, and then more data is collected for further modification. Finally, in addition to examining them each independently, this dissertation also considers the possibility of combining the use of simulated data with importance sampled off-policy data. We combine these sources of auxiliary data by control variate techniques that use simulated data to lower the variance of off-policy policy value estimation. Combining these sources of auxiliary data allows us to introduce two algorithms -- weighted doubly robust bootstrap and model-based bootstrap -- for the problem of lower-bounding the performance of an untested policy
Book Synopsis Artificial Intelligence in Education by : Elisabeth André
Download or read book Artificial Intelligence in Education written by Elisabeth André and published by Springer. This book was released on 2017-06-22 with total page 699 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 18th International Conference on Artificial Intelligence in Education, AIED 2017, held in Wuhan, China, in June/July 2017. The 36 revised full papers presented together with 4 keynotes, 37 poster, presentations, 4 doctoral consortium papers, 5 industry papers, 4 workshop abstracts, and 2 tutorial abstracts were carefully reviewed and selected from 159 submissions. The conference provides opportunities for the cross-fertilization of approaches, techniques and ideas from the many fields that comprise AIED, including computer science, cognitive and learning sciences, education, game design, psychology, sociology, linguistics as well as many domain-specific areas.
Book Synopsis Journal of Machine Learning Research by :
Download or read book Journal of Machine Learning Research written by and published by . This book was released on 2007 with total page 1416 pages. Available in PDF, EPUB and Kindle. Book excerpt: An international forum covering all areas of machine learning.
Book Synopsis The Cross-Entropy Method by : Reuven Y. Rubinstein
Download or read book The Cross-Entropy Method written by Reuven Y. Rubinstein and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rubinstein is the pioneer of the well-known score function and cross-entropy methods. Accessible to a broad audience of engineers, computer scientists, mathematicians, statisticians and in general anyone, theorist and practitioner, who is interested in smart simulation, fast optimization, learning algorithms, and image processing.
Book Synopsis Deep Reinforcement Learning and Its Industrial Use Cases by : Shubham Mahajan
Download or read book Deep Reinforcement Learning and Its Industrial Use Cases written by Shubham Mahajan and published by John Wiley & Sons. This book was released on 2024-10-01 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premise—enabling machines to learn optimal actions within complex environments through trial and error—has broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. “Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications” is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you’re an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency.
Book Synopsis Reinforcement Learning by : Phil Winder Ph.D.
Download or read book Reinforcement Learning written by Phil Winder Ph.D. and published by O'Reilly Media. This book was released on 2020-11-06 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcementand enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learnnumerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website
Book Synopsis Multi-Objective Decision Making by : Diederik M. Zhou
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.
