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Author : Saeed Marzban
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (131 download)
Download or read book Deep Reinforcement Learning for Dynamic Expectile Risk Measures written by Saeed Marzban and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Saeed Marzban
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)
Download or read book Deep Reinforcement Learning for Option Pricing and Hedging Under Dynamic Expectile Risk Measures written by Saeed Marzban and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Saeed Marzban
Publisher :
ISBN 13 :
Total Pages : 146 pages
Book Rating : 4.:/5 (128 download)
Download or read book Equa-risk Pricing, Hedging, and Portfolio Management Using Dynamic Risk Measures and Deep Reinforcement Learning Methods written by Saeed Marzban and published by . This book was released on 2021 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Leslie Pack Kaelbling
Publisher : Springer
ISBN 13 : 0585336563
Total Pages : 286 pages
Book Rating : 4.5/5 (853 download)
Download or read book Recent Advances in Reinforcement Learning written by Leslie Pack Kaelbling and published by Springer. This book was released on 2007-08-28 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
Author : Lucian Busoniu
Publisher : CRC Press
ISBN 13 : 1439821097
Total Pages : 280 pages
Book Rating : 4.4/5 (398 download)
Download or read book Reinforcement Learning and Dynamic Programming Using Function Approximators written by Lucian Busoniu and published by CRC Press. This book was released on 2017-07-28 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.
Author : Tohid Atashbar
Publisher : International Monetary Fund
ISBN 13 :
Total Pages : 32 pages
Book Rating : 4.4/5 (2 download)
Download or read book Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects written by Tohid Atashbar and published by International Monetary Fund. This book was released on 2022-12-16 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this paper, after a theoretical introduction to deep reinforcement learning and various DRL algorithms, we provide an overview of the literature on deep reinforcement learning in economics, with a focus on the main applications of deep reinforcement learning in macromodeling. Then, we analyze the potentials and limitations of deep reinforcement learning in macroeconomics and identify a number of issues that need to be addressed in order for deep reinforcement learning to be more widely used in macro modeling.
Author : Ashwin Rao
Publisher : CRC Press
ISBN 13 : 1000801101
Total Pages : 658 pages
Book Rating : 4.0/5 (8 download)
Download or read book Foundations of Reinforcement Learning with Applications in Finance written by Ashwin Rao and published by CRC Press. This book was released on 2022-12-16 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas — especially finance. Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging. This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code, along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners. Features Focus on the foundational theory underpinning Reinforcement Learning and software design of the corresponding models and algorithms Suitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related courses Suitable for a professional audience of quantitative analysts or data scientists Blends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding To access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book
Author : Prashanth L. A.
Publisher :
ISBN 13 : 9781638280262
Total Pages : 170 pages
Book Rating : 4.2/5 (82 download)
Download or read book Risk-Sensitive Reinforcement Learning Via Policy Gradient Search written by Prashanth L. A. and published by . This book was released on 2022-06-15 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) is one of the foundational pillars of artificial intelligence and machine learning. An important consideration in any optimization or control problem is the notion of risk, but its incorporation into RL has been a fairly recent development. This monograph surveys research on risk-sensitive RL that uses policy gradient search.The authors survey some of the recent work in this area specifically where policy gradient search is the solution approach. In the first risk-sensitive RL setting, they cover popular risk measures based on variance, conditional value at-risk and chance constraints, and present a template for policy gradient-based risk-sensitive RL algorithms using a Lagrangian formulation. For the setting where risk is incorporated directly into the objective function, they consider an exponential utility formulation, cumulative prospect theory, and coherent risk measures.Written for novices and experts alike the authors have made the text completely self-contained but also organized in a manner that allows expert readers to skip background chapters. This is a complete guide for students and researchers working on this aspect of machine learning.
Author : Matthias Heger
Publisher :
ISBN 13 :
Total Pages : 70 pages
Book Rating : 4.:/5 (755 download)
Download or read book Risk and reinforcement learning written by Matthias Heger and published by . This book was released on 1994 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Amir Mosavi
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics written by Amir Mosavi and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.
Author : Lucian Busoniu
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781548919337
Total Pages : 370 pages
Book Rating : 4.9/5 (193 download)
Download or read book Reinforcement Learning and Dynamic Programming Using Function Approximators written by Lucian Busoniu and published by Createspace Independent Publishing Platform. This book was released on 2017-07-17 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement Learning and Dynamic Programming Using Function Approximators By Lucian Busoniu
Author : Ruicheng Liu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Machine-Learning-enhanced Systemic Risk Measure written by Ruicheng Liu and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper explores ways to improve the existing systemic risk measures by incorporating machine learning algorithms into the measurement. We aim to overcome the shortcomings of existing methods that rely on restricted modeling and are difficult to tap into various data resources. To this end, this paper unifies a dynamic quantification framework for systemic risk and links it to a two-step supervised learning problem, which allows for hierarchical structure of the systemic event and the return dependence. We leverage the generalization and predictive powers of machine learning to statistically model the tail events and the co-movements of the equity returns during the shocks to the macro-economy. Our results show that most machine learning algorithms enhance the systemic risk measure's predictive power. Numerous comparative and sensitivity backtesting studies for United States and Hong Kong markets are conducted, from which we recommend the best machine learning algorithm for systemic risk measurement.
Author : Samson Qian
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)
Download or read book Multi-agent Deep Reinforcement Learning and GAN-based Market Simulation for Derivatives Pricing and Dynamic Hedging written by Samson Qian and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancements in computing capabilities have enabled machine learning algorithms to learn directly from large amounts of data. Deep reinforcement learning is a particularly powerful method that uses agents to learn by interacting with an environment of data. Although many traders and investment managers rely on traditional statistical and stochastic methods to price assets and develop trading and hedging strategies, deep reinforcement learning has proven to be an effective method to learn optimal policies for pricing and hedging. Machine learning removes the need for various parametric assumptions about underlying market dynamics by learning directly from data. This research examines the use of machine learning methods to develop a data-driven method of derivatives pricing and dynamic hedging. Nevertheless, machine learning methods like reinforcement learning require an abundance of data to learn. We explore the implementation of a generative adversarial network-based approach to generate realistic market data from past historical data. This data is used to train the reinforcement learning framework and evaluate its robustness. The results demonstrate the efficacy of deep reinforcement learning methods to price derivatives and hedge positions in the proposed systematic GAN-based market simulation framework.
Author : Fuh Yang Goay
Publisher :
ISBN 13 :
Total Pages : 98 pages
Book Rating : 4.:/5 (112 download)
Download or read book A Dynamic Adaptive Streaming System Based on Deep Reinforcement Learning written by Fuh Yang Goay and published by . This book was released on 2019 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Kent Osband
Publisher : Columbia University Press
ISBN 13 : 0231151721
Total Pages : 306 pages
Book Rating : 4.2/5 (311 download)
Download or read book Pandora's Risk written by Kent Osband and published by Columbia University Press. This book was released on 2011 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Author of the acclaimed work Iceberg Risk: An Adventure in Portfolio Theory, Kent Osband argues that uncertainty is central rather than marginal to finance. Markets don't trade mainly on changes in risk. They trade on changes in beliefs about risk, and in the process, markets unite, stretch, and occasionally defy beliefs. Recognizing this truth would make a world of difference in investing. Belittling uncertainty has created a rift between financial theory and practice and within finance theory itself, misguiding regulation and stoking huge financial imbalances. Sparking a revolution in the mindset of the investment professional, Osband recasts the market as a learning machine rather than a knowledge machine. The market continually errs, corrects itself, and makes new errors. Respecting that process, without idolizing it, will promote wiser investment, trading, and regulation. With uncertainty embedded at its core, Osband's rational approach points to a finance theory worthy of twenty-first-century investing.
Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)
Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Author : Rudiger Frey
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (741 download)
Download or read book Quantitative Risk Management written by Rudiger Frey and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
