Learning in embedded systems

Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience wi...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kaelbling, Leslie Pack [verfasserIn]

Format:	E-Book
Sprache:	Englisch

Erschienen:	Cambridge, Mass: MIT Press ; c1993

Schlagwörter:	Computer algorithms Embedded computer systems, Programming Embedded computer systems ; Programming

Systematik:	ST 285

Anmerkung:	"A Bradford book Includes bibliographical references (p. [167]-174) and index
Umfang:	Online-Ressource (xi, 176 p) ; ill

Reproduktion:	Online-Ausg.

Reihe:	Report ; no. STAN-CS-90-1326

Links:	Link aufrufen IEEE Xplore Inhaltsverzeichnis
ISBN:	0-262-28850-8 0-262-11174-8 0-262-51278-5 978-0-262-28850-7 978-0-262-11174-4 978-0-262-51278-7

Katalog-ID:	816666946

Internformat


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Indexfelder

author_variant	l p k lp lpk
matchkey_str	book:9780262288507:1993----
oclc_num	827013107
hierarchy_sort_str	c1993
bklnumber	54.72
callnumber-subject-code	QA
publishDate	1993
allfields	0262288508 electronic bk. 0-262-28850-8 0262111748 0-262-11174-8 0262512785 0-262-51278-5 9780262288507 : electronic bk. 978-0-262-28850-7 9780262111744 978-0-262-11174-4 9780262512787 978-0-262-51278-7 (DE-627)816666946 (DE-576)9816666944 (DE-599)GBV816666946 (OCoLC)827013107 (MITPRESS)6267472 (EBP)055119913 DE-627 ger DE-627 rakwb eng XD-US QA76.6 ST 285 rvk (DE-625)rvk/143648: 54.72 bcl Kaelbling, Leslie Pack verfasserin aut Learning in embedded systems Leslie Pack Kaelbling Cambridge, Mass MIT Press c1993 Online-Ressource (xi, 176 p) ill Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Report no. STAN-CS-90-1326 "A Bradford book Includes bibliographical references (p. [167]-174) and index Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University. Online-Ausg. Computer algorithms Embedded computer systems Programming Embedded computer systems ; Programming Computer algorithms 9780262512787 Print version Learning in embedded systems (DLC)92024672 https://ieeexplore.ieee.org/book/6267472 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext http://www.gbv.de/dms/bowker/toc/9780262111744.pdf V:DE-601 X:Bowker pdf/application 2015-03-18 Verlag Inhaltsverzeichnis Inhaltsverzeichnis ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 ST 285 Computer supported cooperative work (CSCW), Groupware Informatik Monografien Software und -entwicklung Computer supported cooperative work (CSCW), Groupware (DE-627)1270877453 (DE-625)rvk/143648: (DE-576)200877453 BO 045F 006.3/1 045F 005.1 22 01 0018 384846988X olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1521012342 olr-MIT i z 31-01-15 100 01 3100 4472463423 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011216305 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740751010 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267472 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267472 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267472 100 01 3100 https://ieeexplore.ieee.org/book/6267472 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267472 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267472 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267472 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 23 01 0830 2015.01.31 370 01 4370 2021.12.01
spelling	0262288508 electronic bk. 0-262-28850-8 0262111748 0-262-11174-8 0262512785 0-262-51278-5 9780262288507 : electronic bk. 978-0-262-28850-7 9780262111744 978-0-262-11174-4 9780262512787 978-0-262-51278-7 (DE-627)816666946 (DE-576)9816666944 (DE-599)GBV816666946 (OCoLC)827013107 (MITPRESS)6267472 (EBP)055119913 DE-627 ger DE-627 rakwb eng XD-US QA76.6 ST 285 rvk (DE-625)rvk/143648: 54.72 bcl Kaelbling, Leslie Pack verfasserin aut Learning in embedded systems Leslie Pack Kaelbling Cambridge, Mass MIT Press c1993 Online-Ressource (xi, 176 p) ill Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Report no. STAN-CS-90-1326 "A Bradford book Includes bibliographical references (p. [167]-174) and index Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University. Online-Ausg. Computer algorithms Embedded computer systems Programming Embedded computer systems ; Programming Computer algorithms 9780262512787 Print version Learning in embedded systems (DLC)92024672 https://ieeexplore.ieee.org/book/6267472 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext http://www.gbv.de/dms/bowker/toc/9780262111744.pdf V:DE-601 X:Bowker pdf/application 2015-03-18 Verlag Inhaltsverzeichnis Inhaltsverzeichnis ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 ST 285 Computer supported cooperative work (CSCW), Groupware Informatik Monografien Software und -entwicklung Computer supported cooperative work (CSCW), Groupware (DE-627)1270877453 (DE-625)rvk/143648: (DE-576)200877453 BO 045F 006.3/1 045F 005.1 22 01 0018 384846988X olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1521012342 olr-MIT i z 31-01-15 100 01 3100 4472463423 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011216305 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740751010 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267472 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267472 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267472 100 01 3100 https://ieeexplore.ieee.org/book/6267472 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267472 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267472 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267472 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 23 01 0830 2015.01.31 370 01 4370 2021.12.01
allfields_unstemmed	0262288508 electronic bk. 0-262-28850-8 0262111748 0-262-11174-8 0262512785 0-262-51278-5 9780262288507 : electronic bk. 978-0-262-28850-7 9780262111744 978-0-262-11174-4 9780262512787 978-0-262-51278-7 (DE-627)816666946 (DE-576)9816666944 (DE-599)GBV816666946 (OCoLC)827013107 (MITPRESS)6267472 (EBP)055119913 DE-627 ger DE-627 rakwb eng XD-US QA76.6 ST 285 rvk (DE-625)rvk/143648: 54.72 bcl Kaelbling, Leslie Pack verfasserin aut Learning in embedded systems Leslie Pack Kaelbling Cambridge, Mass MIT Press c1993 Online-Ressource (xi, 176 p) ill Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Report no. STAN-CS-90-1326 "A Bradford book Includes bibliographical references (p. [167]-174) and index Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University. Online-Ausg. Computer algorithms Embedded computer systems Programming Embedded computer systems ; Programming Computer algorithms 9780262512787 Print version Learning in embedded systems (DLC)92024672 https://ieeexplore.ieee.org/book/6267472 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext http://www.gbv.de/dms/bowker/toc/9780262111744.pdf V:DE-601 X:Bowker pdf/application 2015-03-18 Verlag Inhaltsverzeichnis Inhaltsverzeichnis ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 ST 285 Computer supported cooperative work (CSCW), Groupware Informatik Monografien Software und -entwicklung Computer supported cooperative work (CSCW), Groupware (DE-627)1270877453 (DE-625)rvk/143648: (DE-576)200877453 BO 045F 006.3/1 045F 005.1 22 01 0018 384846988X olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1521012342 olr-MIT i z 31-01-15 100 01 3100 4472463423 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011216305 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740751010 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267472 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267472 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267472 100 01 3100 https://ieeexplore.ieee.org/book/6267472 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267472 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267472 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267472 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 23 01 0830 2015.01.31 370 01 4370 2021.12.01
allfieldsGer	0262288508 electronic bk. 0-262-28850-8 0262111748 0-262-11174-8 0262512785 0-262-51278-5 9780262288507 : electronic bk. 978-0-262-28850-7 9780262111744 978-0-262-11174-4 9780262512787 978-0-262-51278-7 (DE-627)816666946 (DE-576)9816666944 (DE-599)GBV816666946 (OCoLC)827013107 (MITPRESS)6267472 (EBP)055119913 DE-627 ger DE-627 rakwb eng XD-US QA76.6 ST 285 rvk (DE-625)rvk/143648: 54.72 bcl Kaelbling, Leslie Pack verfasserin aut Learning in embedded systems Leslie Pack Kaelbling Cambridge, Mass MIT Press c1993 Online-Ressource (xi, 176 p) ill Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Report no. STAN-CS-90-1326 "A Bradford book Includes bibliographical references (p. [167]-174) and index Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University. Online-Ausg. Computer algorithms Embedded computer systems Programming Embedded computer systems ; Programming Computer algorithms 9780262512787 Print version Learning in embedded systems (DLC)92024672 https://ieeexplore.ieee.org/book/6267472 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext http://www.gbv.de/dms/bowker/toc/9780262111744.pdf V:DE-601 X:Bowker pdf/application 2015-03-18 Verlag Inhaltsverzeichnis Inhaltsverzeichnis ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 ST 285 Computer supported cooperative work (CSCW), Groupware Informatik Monografien Software und -entwicklung Computer supported cooperative work (CSCW), Groupware (DE-627)1270877453 (DE-625)rvk/143648: (DE-576)200877453 BO 045F 006.3/1 045F 005.1 22 01 0018 384846988X olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1521012342 olr-MIT i z 31-01-15 100 01 3100 4472463423 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011216305 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 3740751010 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6267472 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6267472 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6267472 100 01 3100 https://ieeexplore.ieee.org/book/6267472 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6267472 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6267472 2015 01 DE-93 https://ieeexplore.ieee.org/book/6267472 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 23 01 0830 2015.01.31 370 01 4370 2021.12.01
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abstract	Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University. "A Bradford book Includes bibliographical references (p. [167]-174) and index
abstractGer	Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University. "A Bradford book Includes bibliographical references (p. [167]-174) and index
abstract_unstemmed	Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University. "A Bradford book Includes bibliographical references (p. [167]-174) and index
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title_short	Learning in embedded systems
url	https://ieeexplore.ieee.org/book/6267472 http://www.gbv.de/dms/bowker/toc/9780262111744.pdf
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fulltext	Acknowledgments Introduction … Foundations … Previous Approaches … Interval Estimation Method … Divide and Conquer … Learning Boolean Functions in k-DNF … A Generate-and-Test Algorithm … Learning Action Maps with State … Delayed Reinforcement … Experiments in Complex Domains … Conclusion … Appendix A: Statistics in GTRL … Appendix B: Simplifying Boolean Expressions in GTRL … References … Index …
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