Verfügbarkeit: Differential equation based framework for deep reinforcement learning

Differential equation based framework for deep reinforcement learning:

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Bibliographische Detailangaben
Beteilige Person:	Gottschalk, Simon 1990- (VerfasserIn)
Format:	Hochschulschrift/Dissertation Buch
Sprache:	Englisch
Veröffentlicht:	Stuttgart Fraunhofer Verlag [2021]
Schlagwörter:	Maschinelles Lernen Datenanalyse Differentialgleichung Deep Reinforcement Learning Optimal control Necessary optimality conditions Machine Learning Applied Mathematics Optimization Mathematiker Informatiker Data Scientists Hochschulschrift
Links:	http://deposit.dnb.de/cgi-bin/dokserv?id=ba6f57f463454af88bbd5791c954a581&prov=M&dok_var=1&dok_ext=htm https://d-nb.info/1227947135/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032737289&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	130 Seiten Illustrationen, Diagramme 21 cm
ISBN:	9783839616826 3839616824

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Datensatz im Suchindex

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adam_text	CONTENTS INTRODUCTION 13 1 OPTIMAL CONTROL PROBLEMS AND CORRESPONDING SOLUTION METHODS 19 1.1 INTRODUCTION BASED ON FUNCTIONAL ANALYSIS ............................................. 19 1.2 OPTIMAL CONTROL PROBLEMS .............................................................................. 21 1.3 SOLUTION STRATEGIES .......................................................................................... 23 2 REINFORCEMENT LEARNING 27 2.1 THE MARKOV DECISION PROCESS ....................................................................... 27 2.2 BASIC CONCEPTS AND TECHNIQUE CLASSES ....................................................... 32 2.2.1 THE WORKING PRINCIPLE ....................................................................... 34 2.2.2 VALUE FUNCTION BASED REINFORCEMENT LEARNING .............................. 35 2.2.3 POLICY BASED REINFORCEMENT LEARNING .............................................. 38 2.2.4 ACTOR-CRITIC REINFORCEMENT LEARNING .............................................. 44 2.2.5 MODEL-BASED REINFORCEMENT LEARNING .............................................. 45 2.3 COMPARISON OF OPTIMIZATION CONCEPTS ....................................................... 46 2.4 DEEP REINFORCEMENT LEARNING ....................................................................... 48 2.4.1 THE NEURAL NETWORK .......................................................................... 48 2.4.2 THE VALUE FUNCTION ............................................................................. 52 2.4.3 THE POLICY ............................................................................................. 54 3 THE DIFFERENTIAL EQUATION BASED FRAMEWORK 59 3.1 DERIVATION OF THE INFINITE DIMENSIONAL OPTIMIZATION PROBLEM .................................................................................... 59 3.2 THE NECESSARY OPTIMALITY CONDITIONS .......................................................... 65 3.2.1 SHOOTING METHOD FOR THE OPTIMALITY CONDITIONS .......................... 73 3.2.2 GRADIENT METHOD IN FUNCTION SPACE ................................................. 76 3.3 REINFORCEMENT LEARNING TECHNIQUE BASED ON NECESSARY OPTIMALITY CONDITIONS .......................................................................................................... 77 3.3.1 THE MODEL-FREE ALGORITHM ................................................................. 77 3.3.2 COMPARISON TO REINFORCE .......................................................... 79 3.3.3 FREE TERMINAL PSEUDO TIME ............................................................. 82 3.4 FLEXIBLE NETWORK STRUCTURE BY ADAPTIVE STEP SIZES .................................. 84 3.5 FINITE STATE SPACE .......................................................................................... 86 3.6 EN- AND DECODER ............................................................................................. 88 9 4 APPLICATIONS 91 4.1 THE MOVING POINT IN THE 2D PLANE .............................................................. 92 4.2 ROBOT ................................................................................................................. 96 4.3 HUMAN ARM ..................................................................................................... 107 CONCLUSION & OUTLOOK 116 LIST OF ABBREVIATIONS 117 10
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spellingShingle	Gottschalk, Simon 1990- Differential equation based framework for deep reinforcement learning Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd Differentialgleichung (DE-588)4012249-9 gnd
subject_GND	(DE-588)4193754-5 (DE-588)4123037-1 (DE-588)4012249-9 (DE-588)4113937-9
title	Differential equation based framework for deep reinforcement learning
title_auth	Differential equation based framework for deep reinforcement learning
title_exact_search	Differential equation based framework for deep reinforcement learning
title_full	Differential equation based framework for deep reinforcement learning Simon Gottschalk
title_fullStr	Differential equation based framework for deep reinforcement learning Simon Gottschalk
title_full_unstemmed	Differential equation based framework for deep reinforcement learning Simon Gottschalk
title_short	Differential equation based framework for deep reinforcement learning
title_sort	differential equation based framework for deep reinforcement learning
topic	Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd Differentialgleichung (DE-588)4012249-9 gnd
topic_facet	Maschinelles Lernen Datenanalyse Differentialgleichung Hochschulschrift
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