Adaptive representations for reinforcement learning:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg
Springer
2010
|
| Series: | Studies in computational intelligence
291 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XIII, 116 S. graph. Darst. |
| ISBN: | 9783642139314 |
Staff View
MARC
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Record in the Search Index
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CONTENTS 1 INTRODUCTION 1 1.1 MOTIVATION 2 1.2 APPROACH 2 1.3 OVERVIEW 4
2 REINFORCEMENT LEARNING 7 2.1 REINFORCEMENT LEARNING FRAMEWORK 7 2.2
TEMPORAL DIFFERENCE METHODS 9 2.3 POLICY SEARCH METHODS 11 3 ON-LINE
EVOLUTIONARY COMPUTATION 17 3.1 E-GREEDY EVOLUTION 20 3.2 SOFTMAX
EVOLUTION 21 3.3 INTERVAL ESTIMATION EVOLUTION 22 3.4 TESTBED DOMAINS 22
3.4.1 MOUNTAIN CAR 23 3.4.2 SERVER JOB SCHEDULING 24 3.5 RESULTS 26 3.6
DISCUSSION 28 4 EVOLUTIONARY FUNCTION APPROXIMATION 31 4.1 NEAT+Q 33 4.2
RESULTS 34 4.2.1 COMPARING MANUAL AND EVOLUTIONARY FUNCTION
APPROXIMATION 34 4.2.2 COMBINING ON-LINE EVOLUTION WITH EVOLUTIONARY
FUNCTION APPROXIMATION 37 4.2.3 COMPARING TO OTHER APPROACHES 38 4.2.4
COMPARING DARWINIAN AND LAMARCKIAN APPROACHES 42 BIBLIOGRAFISCHE
INFORMATIONEN HTTP://D-NB.INFO/1002680387 DIGITALISIERT DURCH 8.4.3
FS-NEAT 93 XII CONTENTS 4.2.5 CONTINUAL LEARNING TESTS 43 4.3 DISCUSSION
45 5 SAMPLE-EFFICIENT EVOLUTIONARY FUNCTION APPROXIMATION 47 5.1
SAMPLE-EFFICIENT NEAT+Q 47 5.2 RESULTS 49 5.3 DISCUSSION 52 6 AUTOMATIC
FEATURE SELECTION FOR REINFORCEMENT LEARNING 53 6.1 FS-NEAT 54 6.2
TESTBED DOMAIN 56 6.3 RESULTS 58 6.4 DISCUSSION 61 7 ADAPTIVE TILE
CODING 65 7.1 BACKGROUND 66 7.1.1 TILE CODING 66 7.2 METHOD 68 7.2.1
WHEN TO SPLIT 69 7.2.2 WHERE TO SPLIT 70 SUBSUBSECTION.7.2.2.1
SUBSUBSECTION.7.2.2.2 7.3 TESTBED DOMAINS 72 7.4 RESULTS 73 7.5
DISCUSSION 74 8 RELATED WORK 77 8.1 OPTIMIZING REPRESENTATIONS 77 8.1.1
SUPERVISED LEARNING 77 8.1.2 REINFORCEMENT LEARNING 78 8.1.3
EVOLUTIONARY COMPUTATION 82 8.2 COMBINING EVOLUTION AND LEARNING 84
8.2.1 APPLICATIONS TO SUPERVISED LEARNING 84 8.2.2 APPLICATIONS TO
REINFORCEMENT LEARNING 86 8.3 BALANCING EXPLORATION AND EXPLOITATION 89
8.3.1 FC-ARMED BANDIT PROBLEM 89 8.3.2 ASSOCIATIVE SEARCH 90 8.3.3
REINFORCEMENT LEARNING 90 8.4 FEATURE SELECTION 92 8.4.1 FILTERS 92
8.4.2 WRAPPERS 93 REFERENCES 107 CONTENTS XIII 9 CONCLUSION 95 9.1
PRIMARY CONCLUSIONS 95 9.2 NEGATIVE RESULTS 96 9.2.1 COMBINING FS-NEAT
WITH NEAT+Q 96 9.2.2 FEATURE SELECTION IN ADAPTIVE TILE CODING 97 9.2.3
FITNESS FUNCTIONS BASED ON BELLMAN ERROR 97 9.3 BROADER IMPLICATIONS 98
9.3.1 STOCHASTIC VS. DETERMINISTIC DOMAINS 98 9.3.2 THE VALUE FUNCTION
GAMBLE 99 9.3.3 THE ROLE OF SEARCH IN ADAPTIVE REPRESENTATIONS 100 9.4
FUTURE WORK 102 9.4.1 NON-STATIONARITY 102 9.4.2 STEADY-STATE
EVOLUTIONARY COMPUTATION 102 9.4.3 MODEL-BASED REINFORCEMENT LEARNING
103 9.5 FINAL REMARKS 104 A STATISTICAL SIGNIFICANCE 105 |
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| illustrated | Illustrated |
| indexdate | 2024-07-20T10:52:43Z |
| institution | BVB |
| isbn | 9783642139314 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020716201 |
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| physical | XIII, 116 S. graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Springer |
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| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Whiteson, Shimon Verfasser aut Adaptive representations for reinforcement learning Shimon Whiteson Berlin ; Heidelberg Springer 2010 XIII, 116 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 291 Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd rswk-swf Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 s DE-604 Erscheint auch als Online-Ausgabe Adaptive Representations for Reinforcement Learning Studies in computational intelligence 291 (DE-604)BV020822171 291 text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3480844&prov=M&dok%5Fvar=1&dok%5Fext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020716201&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Whiteson, Shimon Adaptive representations for reinforcement learning Studies in computational intelligence Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd |
| subject_GND | (DE-588)4825546-4 |
| title | Adaptive representations for reinforcement learning |
| title_auth | Adaptive representations for reinforcement learning |
| title_exact_search | Adaptive representations for reinforcement learning |
| title_full | Adaptive representations for reinforcement learning Shimon Whiteson |
| title_fullStr | Adaptive representations for reinforcement learning Shimon Whiteson |
| title_full_unstemmed | Adaptive representations for reinforcement learning Shimon Whiteson |
| title_short | Adaptive representations for reinforcement learning |
| title_sort | adaptive representations for reinforcement learning |
| topic | Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd |
| topic_facet | Bestärkendes Lernen Künstliche Intelligenz |
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| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT whitesonshimon adaptiverepresentationsforreinforcementlearning |