Reinforcement Learning and Dynamic Programming Using Function Approximators
While Dynamic Programming (DP) has helped solve control problems involving dynamic systems, its value was limited by algorithms that lacked practical scale-up capacity. In recent years, developments in Reinforcement Learning (RL), DP's model-free counterpart, has changed this. Focusing on conti...
Saved in:
| Main Authors | , , , |
|---|---|
| Format | eBook Book Web Resource |
| Language | English |
| Published |
Boca Raton
CRC Press
2010
Taylor & Francis Group |
| Edition | 1 |
| Series | Automation and control engineering |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | While Dynamic Programming (DP) has helped solve control problems involving dynamic systems, its value was limited by algorithms that lacked practical scale-up capacity. In recent years, developments in Reinforcement Learning (RL), DP's model-free counterpart, has changed this. Focusing on continuous-variable problems, this unparalleled work provides an introduction to classical RL and DP, followed by a presentation of current methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, it offers illustrative examples that readers will be able to adapt to their own work. |
|---|---|
| AbstractList | 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 Approximatorsprovides 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. While Dynamic Programming (DP) has helped solve control problems involving dynamic systems, its value was limited by algorithms that lacked practical scale-up capacity. In recent years, developments in Reinforcement Learning (RL), DP's model-free counterpart, has changed this. Focusing on continuous-variable problems, this unparalleled work provides an introduction to classical RL and DP, followed by a presentation of current methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, it offers illustrative examples that readers will be able to adapt to their own work. |
| Author | Ernst, Damien De Schutter, Bart Busoniu, Lucian Babuska, Robert |
| Author_xml | – sequence: 1 givenname: Lucian surname: Busoniu fullname: Busoniu, Lucian – sequence: 2 givenname: Robert surname: Babuska fullname: Babuska, Robert – sequence: 3 givenname: Bart surname: De Schutter fullname: De Schutter, Bart – sequence: 4 givenname: Damien surname: Ernst fullname: Ernst, Damien |
| BackLink | https://cir.nii.ac.jp/crid/1130000798311682304$$DView record in CiNii |
| BookMark | eNpVkc9PHCEUxzG1TdV69D6HxsTDWh4w_DjadW2bbqIxtlfCMMyWOgMWZlf978s4XuTweDw-j3zfl0O0H2JwCJ0APgeC4YsSEhhVkgBWsIcOgUJNQCgq3pXD641UH9AB55wWmPOP6Djnv7gsRrgi9QH6eet86GKybnBhrNbOpODDpjKhrS6fgxm8rW5S3CQzDFP9V57i1TbY0cdQXTw8pPjkBzPGlD-h953pszt-3Y_Q76vV3fL7Yn397cfyYr0wjNVcLrjk2Nq2BcyZILITbdtR2zALomZd6wwwUdsGU9LIzhhaK0nBSoaJ48TWQI8QmR_uvds4HVPj9Y7oaPycb_uNNlY3ThPCpSZCcVqazuYmk-_dY_4T-zHrXe-aGO-zfmNmYU9ntkz3b-vyqF8wWyxKpterr8taKilIAT_PYPBeWz9FADoZLCbRwCWhmBVsNWMvXg_mMaa-1aN57mPqkgnW51kGYD397Vs5eudSLm4T-h_MA5Wu |
| ContentType | eBook Book Web Resource |
| Copyright | 2010 by Taylor & Francis Group, LLC |
| Copyright_xml | – notice: 2010 by Taylor & Francis Group, LLC |
| DBID | RYH Q33 |
| DEWEY | 621.317 |
| DOI | 10.1201/9781439821091 |
| DatabaseName | CiNii Complete Université de Liège - Open Repository and Bibliography (ORBI) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISBN | 1315217937 9781315217932 1439821097 9781439821091 |
| Edition | 1 |
| ExternalDocumentID | oai_orbi_ulg_ac_be_2268_27963 9781439821091 EBC589872 BB02857990 10_1201_9781439821091_version2 |
| GroupedDBID | 089 20A 38. 5~G 92K A4J AABBV AADZT ABARN ABEQL ABQPQ ACLGV ADVEM ADYHE AEEBL AERYV AEUHU AFIZQ AFOJC AFXGA AGOQD AHEBD AHTWU AHWGJ AIENH AIXXW AJFER AKSCQ ALMA_UNASSIGNED_HOLDINGS ALYTH AQBOK ATPON AZZ BBABE BMO BOEYA BQVRA CZZ EBATF GEO GEOUK I4C INALI JG1 JTX MYL NEQ NEV PQQKQ S.B ABBFG ABYEM ACGYG ACNUM AKQZE RYH ABYSD AXTGW Q33 |
| ID | FETCH-LOGICAL-a44568-6860ccdd1064728f7ddf3cb4c1754fdea1475cb032b8faa359831c8402e62c513 |
| ISBN | 1439821089 9781439821084 |
| IngestDate | Fri Aug 01 18:58:41 EDT 2025 Fri Nov 08 04:52:03 EST 2024 Wed Aug 27 02:26:44 EDT 2025 Fri Jun 27 00:11:57 EDT 2025 Tue Aug 12 08:52:16 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | false |
| Keywords | Approximate Policy Policy Search Parameter Vector Execution Time RL Method Bellman Optimality Equation Reward Function Fuzzy Approximator Inverted Pendulum Continuous Action Control Pi Approximate DP Bellman Equation Inverted Pendulum Problem Policy Improvement Step DC Motor Policy Iteration Algorithms State Action Pair Stochastic Case Triangular MFs Cross-entropy Method Fuzzy Partition RL Algorithm Policy Improvement Deterministic Case |
| LCCallNum_Ident | TJ223.M53 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a44568-6860ccdd1064728f7ddf3cb4c1754fdea1475cb032b8faa359831c8402e62c513 |
| Notes | Includes bibliographical references and index scopus-id:2-s2.0-85046476577 Automation & control engineering |
| OCLC | 666378166 |
| OpenAccessLink | http://orbi.ulg.ac.be/handle/2268/27963 |
| PQID | EBC589872 |
| PageCount | 286 |
| ParticipantIDs | liege_orbi_v2_oai_orbi_ulg_ac_be_2268_27963 askewsholts_vlebooks_9781439821091 proquest_ebookcentral_EBC589872 nii_cinii_1130000798311682304 informaworld_taylorfrancisbooks_10_1201_9781439821091_version2 |
| PublicationCentury | 2000 |
| PublicationDate | 2010 c2010 2010-04-29 2010-04 |
| PublicationDateYYYYMMDD | 2010-01-01 2010-04-29 2010-04-01 |
| PublicationDate_xml | – year: 2010 text: 2010 |
| PublicationDecade | 2010 |
| PublicationPlace | Boca Raton |
| PublicationPlace_xml | – name: Boca Raton – name: Milton |
| PublicationSeriesTitle | Automation and control engineering |
| PublicationYear | 2010 |
| Publisher | CRC Press Taylor & Francis Group |
| Publisher_xml | – name: CRC Press – name: Taylor & Francis Group |
| RestrictionsOnAccess | open access |
| SSID | ssj0000426925 ssib030939509 |
| Score | 2.4667807 |
| Snippet | While Dynamic Programming (DP) has helped solve control problems involving dynamic systems, its value was limited by algorithms that lacked practical scale-up... From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control... |
| SourceID | liege askewsholts proquest nii informaworld |
| SourceType | Open Access Repository Aggregation Database Publisher |
| SubjectTerms | Computer science Digital control systems Dynamic programming Engineering, computing & technology Ingénierie, informatique & technologie Sciences informatiques |
| TableOfContents | Cover -- Title -- Copyright -- Preface -- About the authors -- Contents -- 1 Introduction -- 2 An introduction to dynamic programming and reinforcement learning -- 3 Dynamic programming and reinforcement learning in large and continuous spaces -- 4 Approximate value iteration with a fuzzy representation -- 5 Approximate policy iteration for online learning and continuous-action control -- 6 Approximate policy search with cross-entropy optimization of basis functions -- Appendix A: Extremely randomized trees -- Appendix B: The cross-entropy method -- Symbols and abbreviations -- Bibliography -- List of algorithms -- Index |
| Title | Reinforcement Learning and Dynamic Programming Using Function Approximators |
| URI | https://www.taylorfrancis.com/books/9781315217932 https://cir.nii.ac.jp/crid/1130000798311682304 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=589872 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781439821091 http://orbi.ulg.ac.be/handle/2268/27963 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3Nb9MwFLfQeqEnPkUYgwhxQwHi2Gl8QWJdpwkEBzTQbpbt2Chal0ltUiH-et5z3DTpJiTgYiWuY7n-OXkffu9nQl45q5k2wiXcwOvGVJEmQnCdZFTBCrG5EBbznT9_yc--sY8X_GJ3tp7PLmn0G_Pr1rySf0EV6gBXzJL9C2T7TqECrgFfKAFhKPeU3_42UGlbT3hqvG9vy5HaJRuedEfMYwYABl5dYX0XF3AKIsyj_QF5xH9WV2hv77zkLWjeVevt9NYMFs2x0u36Uu3CsHvd13N4-oOuw85F_9NiVXe5JCcwlpBsFlwLPkJt6FqYf52PAkE6mxMULFFQ5BO99QtMPfP_jXZ7pNZoekBLOWonN52LECTqhLI8g8_WBITzone_4K6t4IFvzItaTMSlPHCnQodvRx1OyVStL0FYgCBp1ntktGCNLDEuAZSKuqpuiGKvX5zfIxOLSSf3yR1bPyDTAUnkQ_JpBHa8BTsGsOMAdjwAO_Zgx1uw4xHYj8j308X5_CwJR2AkioFqWyR5kb8zpixTTAqmhZuVpcuMZgbUPuZKq1I24wamhurCKYV8jFlqwGqnNqeGp9ljclBf1_YJiRm1IPmco8w55uAB6jQXjiOdk7XcReTlYLbkZum369djjCLyfjiJsvF-JdcdAtO1_zO2EXntp11er3QlN1Qiv7m_bpc_pDJSWwkmQSHpDGRDRI4AHGkqLFPcegWdFv9imvsd4oi82MIm_XBDSLNcHM95IYoZffq_Az4kd3fvxjNy0KxaewSKaKOfh-X5G5D7hWQ |
| linkProvider | ProQuest Ebooks |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.title=Reinforcement+Learning+and+Dynamic+Programming+Using+Function+Approximators&rft.au=Busoniu%2C+Lucian&rft.au=Babuska%2C+Robert&rft.au=De+Schutter%2C+Bart&rft.au=Ernst%2C+Damien&rft.date=2010-01-01&rft.pub=CRC+Press&rft.isbn=9781439821091&rft_id=info:doi/10.1201%2F9781439821091&rft.externalDocID=10_1201_9781439821091_version2 |
| thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fvle.dmmserver.com%2Fmedia%2F640%2F97814398%2F9781439821091.jpg |