LEADER 00875nam0-22003131i-450- 001 990000457530403321 005 20081006105027.0 010 $a0-471-29195-1 035 $a000045753 035 $aFED01000045753 035 $a(Aleph)000045753FED01 035 $a000045753 100 $a20020821d1972----km-y0itay50------ba 101 0 $aeng 105 $aa-------001yy 200 1 $aInteger programming$fRobert S. Garfinkel, George L. Nemhauser 210 $aNew York$cWiley & sons$d©1972 215 $a427 p.$cill.$d24 cm 225 1 $aSeries in decision and control 610 0 $aProgrammazione a numeri interi 676 $a519.77 700 1$aGarfinkel,$bRobert S. 701 1$aNemhauser,$bGeorge L. 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000457530403321 952 $a10 C 237$b1073 CCE$fDINEL 959 $aDINEL 997 $aUNINA LEADER 01506nam 2200361 n 450 001 996396239203316 005 20221108055019.0 035 $a(CKB)4330000000362281 035 $a(EEBO)2248558778 035 $a(UnM)9959024100971 035 $a(EXLCZ)994330000000362281 100 $a19900919d1621 uh 101 0 $aeng 135 $aurbn||||a|bb| 200 10$aBy the King. A proclamation against abuses in preparing and preferring billes and other vvritings to his Maiesties signature$b[electronic resource] 210 $aImprinted at London $cby Bonham Norton, and Iohn Bill, printers to the Kings most Excellent Maiestie$dM. DC. XXI. [1621] 215 $a1 sheet ([1] p.) 300 $aOnly the King's officials may do so, upon authentic warrant--STC. 300 $aArms 14; Steele notation: li- lish exer-. 300 $aReproduction of the original in the British Library. 330 $aeebo-0018 606 $aProcedure (Law)$xLaw and legislation$zGreat Britain$vEarly works to 1800 606 $aBills, Legislative$zEngland$vEarly works to 1800 615 0$aProcedure (Law)$xLaw and legislation 615 0$aBills, Legislative 701 $aJames$cKing of England,$f1566-1625.$01001019 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 906 $aBOOK 912 $a996396239203316 996 $aBy the King. A proclamation against abuses in preparing and preferring billes and other vvritings to his Maiesties signature$92364474 997 $aUNISA LEADER 02388nam 2200505Ia 450 001 9910655305603321 005 20210308191403.0 010 $a1-282-91772-2 010 $a9786612917721 010 $a0-19-978148-6 035 $a(CKB)2670000000069091 035 $a(EBL)618604 035 $a(OCoLC)694088035 035 $a(MiAaPQ)EBC618604 035 $a(EXLCZ)992670000000069091 100 $a20100429d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 200 10$aExorbitant privilege$b[electronic resource] $ethe rise and fall of the dollar and the future of the international monetary system /$fBarry Eichengreen 210 $aOxford ;$aNew York, NY $cOxford University Press$d2010 215 $a1 online resource (224 p.) 300 $aDescription based upon print version of record. 311 $a0-19-993109-7 311 $a0-19-975378-4 320 $aIncludes bibliographical references and index. 327 $aContents; 1 Introduction; 2 Debut; 3 Dominance; 4 Rivalry; 5 Crisis; 6 Monopoly No More; 7 Dollar Crash; Notes; References; Acknowledgments; Index 330 $aFor more than half a century, the U.S. dollar has been not just America's currency but the world's. It is used globally by importers, exporters, investors, governments and central banks alike. Nearly three-quarters of all 100 bills circulate outside the United States. The dollar holdings of the Chinese government alone come to more than 1,000 per Chinese resident. This dependence on dollars, by banks, corporations and governments around the world, is a source of strength for the United States. It is, as a critic of U.S. policies once put it, America's ""exorbitant privilege."" However, recen 606 $aDevaluation of currency$zUnited States$xHistory$y21st century 606 $aFinancial crises$zUnited States$y21st century 606 $aMoney$zUnited States$xHistory$y20th century 607 $aUnited States$xEconomic policy$y2009- 608 $aElectronic books. 615 0$aDevaluation of currency$xHistory 615 0$aFinancial crises 615 0$aMoney$xHistory 676 $a332.4/973 676 $a332.4973 700 $aEichengreen$b Barry J$0318418 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910655305603321 996 $aExorbitant privilege$92813887 997 $aUNINA LEADER 01547nas 2200517-a 450 001 996203163203316 005 20240413021041.0 011 $a1423-0216 035 $a(CKB)954927712310 035 $a(CONSER)sn-94038415- 035 $a(DE-599)ZDB1483035-8 035 $a(EXLCZ)99954927712310 100 $a19940411a19949999 --- - 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNeuroimmunomodulation 210 $aBasel ;$aNew York $cS. 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Battista De Luca sopra alcune cose appartenenti a? caualieri, & alle dame, cosi? nella legge scritta, come in quella della conuenienza, contenute negli argomenti registrati nell'annesso indice 210 $aRome $c[s.n.]$d1675 215 $aOnline resource ([88], 584 p., 4º) 300 $aReproduction of original in Biblioteca Nazionale Centrale di Firenze. 700 $aDe Luca$b Giovanni Battista$f1614-1683.$077549 801 0$bUk-CbPIL 801 1$bUk-CbPIL 906 $aBOOK 912 $a9910481399503321 996 $aIl caualiere e la dama ouero Discorsi familiari nell'ozio Tusculano autunnale dell'anno 1674 di Gio. Battista De Luca sopra alcune cose appartenenti à caualieri, & alle dame, così nella legge scritta, come in quella della conuenienza, contenute negli argomenti registrati nell'annesso indice$91918339 997 $aUNINA LEADER 05718nam 22007695 450 001 9910686481603321 005 20260721193910.0 010 $a9789811977848$b(electronic book) 010 $a9811977844$b(electronic book) 024 7 $a10.1007/978-981-19-7784-8 035 $a(MiAaPQ)EBC7235225 035 $a(Au-PeEL)EBL7235225 035 $a(DE-He213)978-981-19-7784-8 035 $a(OCoLC)1376259618 035 $a(PPN)269659501 035 $a(CKB)26401622000041 035 $a(UkBuK)2340492 035 $a(EXLCZ)9926401622000041 100 $a20230405d2023 u| 0 101 0 $aeng 135 $aurcn#|||a|||a 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReinforcement Learning for Sequential Decision and Optimal Control /$fby Shengbo Eben Li 205 $a1st ed. 2023. 210 1$cSpringer Nature$d2023 215 $a1 online resource (xxx, 462 pages) $ccolor illustrations 311 08$aPrint version: Li, Shengbo Eben Reinforcement Learning for Sequential Decision and Optimal Control Singapore : Springer,c2023 9789811977831 311 08$a9811977836 320 $aIncludes bibliographical references and index. 327 $aChapter 1 Introduction of Reinforcement Learning -- Chapter 2 Principles of RL Problems -- Chapter 3 Model-free Indirect RL: Monte Carlo -- Chapter 4 Model-Free Indirect RL: Temporal-Difference -- Chapter 5 Model-based Indirect RL: Dynamic Programming -- Chapter 6 Indirect RL with Function Approximation -- Chapter 7 Direct RL with Policy Gradient -- Chapter 8 Infinite Horizon Approximate Dynamic Programming -- Chapter 9 Finite Horizon ADP and State Constraints -- Chapter 10 Deep Reinforcement Learning -- Chapter 11 Advanced RL Topics. 330 $aHave you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future. As an interdisciplinary field of trial-and-error learning and optimal control, RL resembles how humans reinforce their intelligence by interacting with the environment and provides a principled solution for sequential decision making and optimal control in large-scale and complex problems. Since RL contains a wide range of new concepts and theories, scholars may be plagued by a number of questions: What is the inherent mechanism of reinforcement learning? What is the internal connection between RL and optimal control? How has RL evolved in the past few decades, and what are the milestones? How do we choose and implement practical and effective RL algorithms for real-world scenarios? What are the key challenges that RL faces today, and how can we solve them? What is the current trend of RL research? You can find answers to all those questions in this book. The purpose of the book is to help researchers and practitioners take a comprehensive view of RL and understand the in-depth connection between RL and optimal control. The book includes not only systematic and thorough explanations of theoretical basics but also methodical guidance of practical algorithm implementations. The book intends to provide a comprehensive coverage of both classic theories and recent achievements, and the content is carefully and logically organized, including basic topics such as the main concepts and terminologies of RL, Markov decision process (MDP), Bellman?s optimality condition, Monte Carlo learning, temporal difference learning, stochastic dynamic programming, function approximation, policy gradient methods, approximate dynamic programming, and deep RL, as well as the latest advances in action and state constraints, safety guarantee, reference harmonization, robust RL, partially observable MDP, multiagent RL, inverse RL, offline RL, and so on. 606 $aMachine learning 606 $aComputational intelligence 606 $aSystem theory 606 $aControl theory 606 $aEngineering mathematics 606 $aAutomatic control 606 $aRobotics 606 $aAutomation 606 $aMachine Learning 606 $aComputational Intelligence 606 $aSystems Theory, Control 606 $aEngineering Mathematics 606 $aControl, Robotics, Automation 615 0$aMachine learning. 615 0$aComputational intelligence. 615 0$aSystem theory. 615 0$aControl theory. 615 0$aEngineering mathematics. 615 0$aAutomatic control. 615 0$aRobotics. 615 0$aAutomation. 615 14$aMachine Learning. 615 24$aComputational Intelligence. 615 24$aSystems Theory, Control. 615 24$aEngineering Mathematics. 615 24$aControl, Robotics, Automation. 676 $a006.31 700 $aLi$b Shengbo Eben$01350676 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910686481603321 996 $aReinforcement Learning for Sequential Decision and Optimal Control$93089380 997 $aUNINA