Gespeichert in:
| Titel: | Transfer in reinforcement learning domains |
|---|---|
| Von: |
Matthew E. Taylor
|
| Person: |
Taylor, Matthew E.
Verfasser aut |
| Hauptverfasser: | |
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin ; Heidelberg
Springer
2009
|
| Schriftenreihe: | Studies in computational intelligence
216 |
| Notation: | ST 300 |
| Schlagwörter: | |
| Medienzugang: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018620060&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Literaturverz. S. 221 - 229 |
| Umfang: | XII, 229 S. graph. Darst. 24 cm |
| ISBN: | 9783642018817 |
Internformat
MARC
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| 100 | 1 | |a Taylor, Matthew E. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Transfer in reinforcement learning domains |c Matthew E. Taylor |
| 264 | 1 | |a Berlin ; Heidelberg |b Springer |c 2009 | |
| 300 | |a XII, 229 S. |b graph. Darst. |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 490 | 1 | |a Studies in computational intelligence |v 216 | |
| 500 | |a Literaturverz. S. 221 - 229 | ||
| 650 | 4 | |a Bestärkendes Lernen <Künstliche Intelligenz> | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Reinforcement learning | |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-018620060 | |
Datensatz im Suchindex
| _version_ | 1819269477714362368 |
|---|---|
| adam_text | Contents
Introduction
............................................. 1
1.1 Problem
Definition
.................................... 4
1.2
Evaluating Transfer Learning Methods
................... 5
1.2.1
Empirical Transfer Comparisons
................... 7
1.2.2
Dimensions of Comparison
....................... 10
1.3
Transfer for Reinforcement Learning
..................... 11
1.3.1
Reinforcement Learning Notation
.........../·...... 11
1.3.2
What Follows
................................... 12
Reinforcement Learning Background
.................... 15
2.1
Framing the Reinforcement Learning Problem
............. 15
2.2
Function Approximation
............................... 17
2.2.1
Cerebellar Model Arithmetic Computers
........... 17
2.2.2
Radial Basis Functions
........................... 18
2.2.3
Artificial Neural Networks
........................ 19
2.2.4
Instance-Based Approximation
.................... 21
2.3
Learning Methods
..................................... 22
2.3.1
Sarsa
.......................................... 23
2.3.2
NeuroEvolution of Augmenting Topologies
(NEAT)
............................ ............ 25
2.3.3
Fitted R-Max
................................... 27
Related Work
........................................... 31
3.1
Transfer Approaches
................................... 31
3.1.1
Transfer Methods Categorization
.................. 32
3.1.2
Multi-Task Learning
............................. 34
3.1.3
Inter-Task Mappings
............................. 36
3.1.4
Related Paradigms
.............................. 38
3.2
Transfer Methods for Fixed State Variables and Actions
.... 39
Contents
3.3
Multi-Task Learning Methods
........................... 44
3.4
Transferring Task-Invariant Knowledge between Tasks
with Differing State Variables and Actions
................ 49
3.5
Explicit Mappings to Transfer between Different Actions
and State Representations
.............................. 52
3.6
Learning Task Mappings
............................... 57
Empirical Domains
...................................... 61
4.1
Generalized Mountain Car
.............................. 61
4.1.1
Two Dimensional Mountain Car
................... 62
4.1.2
Three Dimensional Mountain Car
................. 03
4.1.3
Learning Mountain Car
.......................... 66
4.2
Server Job Scheduling
.................................. 67
4.2.1
Learning Server Job Scheduling
................... 70
4.3
Robot Soccer Keepaway
................................ 71
4.3.1
More Complex Keepaway Tasks
................... 73
4.3.2 3
vs.
2
XOR Keepaway
........................... 75
4.3.3
Additional Keepaway Variants
.................... 76
4.3.4
Learning Keepaway
.............................. 77
4.4
Ringworld
............................................ 83
4.4.1
Learning Ringworld
.............................. 85
4.5
Knight Joust
.......................................... 86
4.5.1
Learning Knight Joust
........................... 88
4.6
Summary of Domains
.................................. 89
Value Function Transfer via Inter-Task Mappings
........ 91
5.1
Inter-Task Mappings
................................... 91
5.1.1
Inter-Task Mappings for the Keepaway Domain
..... 93
5.2
Value Function Transfer
................................ 95
5.2.1
Constructing pc
m
ас
and Prbf
................... 96
5.2.2
Constructing p_ANN
.............................. 98
5.3
Empirical Evaluation of Value Function Transfer
.......... 99
5.3.1
Transfer from
3
vs.
2
Keepaway to
4
vs.
3
Keepaway
...................................... 99
5.3.2
Understanding pc mac s Benefit
................... 105
5.3.3
Transfer between Players with Differing Abilities
.... 110
5.3.4
Transfer from Knight Joust to
4
vs.
3
Keepaway
..... 112
5.3.5
Variants on
3
vs.
2
Keepaway: Negative Transfer
.... 115
5.3.6
Larger Keepaway Tasks and Multi-Step Transfer
.... 116
5.4
On the Applicability of Value Function Transfer
........... 118
Extending Transfer via Inter-Task Mappings
............. 121
6.1
Q-Value Reuse
........................................ 121
6.1.1
Q-Value Reuse Results: Mountain Car
............. 123
6.1.2
Q-Value Reuse Results: Keepaway
................. 125
Contents Xl
6.2
Policy Transfer
........................................ 127
6.2.1 Server Job
Scheduling Results.....................
129
6.2.2 Keepaway
Results...............................
134
6.2.3
Partial Mapping Results
......................... 136
6.3
Chapter Summary
..................................... 138
7
Transfer between Different Reinforcement Learning
Methods
................................................. 139
7.1
TIMBREL: Instance-Based Transfer
..................... 140
7.1.1
Model Transfer
.................................. 141
7.1.2
Implementing TIMBREL in Mountain Car
......... 142
7.1.3
TIMBREL Transfer Experiments
.................. 145
7.1.4
TIMBREL Summary
............................ 148
7.2
Transfer via Rules
..................................... 148
7.2.1
Rule Utilization Schemes
......................... 150
7.2.2
Cross-Domain Rule Transfer Results
............... 153
7.2.3
Rule Transfer:
3
vs.
2
Keepaway to
4
vs.
3
Keepaway
...................................... 161
7.2.4
Rule Transfer Summary
.......................... 161
7.3
Representation Transfer
................................ 163
7.3.1
Complexification
................................ 165
7.3.2
Offline Representation Transfer
................... 166
7.3.3
Overview of Representation Transfer Results
........ 168
7.3.4
Complexification in XOR Keepaway
......... .·■..... 169
7.3.5
Offline Representation Transfer in
3
vs.
2
Keepaway
...................................... 171
7.3.6
Offline Representation Transfer for Task Transfer
.... 175
7.3.7
Representation Transfer Summary
................. 176
7.4
Comparison of Presented Transfer Methods
............... 178
8
Learning Inter-Task Mappings
........................... 181
8.1
Learning Inter-Task Mappings via Classification
........... 181
8.1.1
Learning Keepaway Inter-Task Mapping s
........... 184
8.1.2
Learning Server Job Scheduling Inter-Task
Mappings
...................................... 186
8.1.3
Learned Inter-Task Mapping Results
............... 187
8.1.4
Discussion
...................................... 189
8.2
MASTER: Learning Inter-Task Mappings Offline
.......... 190
8.2.1
MASTER: Empirical Validation
................... 193
8.2.2
Transfer from 2D to
3D
Mountain Car
............. 196
8.2.3
Reducing the Total Sample Complexity
............ 197
8.2.4
Comparison to Previous Work
.................... 199
8.2.5
Transfer in Hand Brake Mountain Car
............. 201
8.3
Chapter Summary
..................................... 203
XII
Contents
9
Conclusion and Future Work
............................ 205
9.1
Summary of Monograph Methods
....................... 205
9.2
Possible Enhancements to Monograph Methods
........... 207
9.2.1
Inter-Task Mappings
............................. 207
9.2.2
Transfer Algorithms
............................. 208
9.3
Determining the Efficacy of Transfer
..................... 211
9.3.1
Avoiding
Negativo»
Transfer
....................... 211
9.4
Future Transfer Work
.................................. 213
9.4.1
Increasing Transfer s Applicability
................. 213
9.4.2
Improving Transfer s Efficacy
..................... 214
9.4.3
Transfer in More Difficult Tasks
................... 217
9.5
Conclusion
............................................ 217
A On-Line Appendix
....................................... 219
References
................................................... 221
|
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| dewey-search | 006.31 006.3/1 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV035760176 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:59:33Z |
| institution | BVB |
| isbn | 9783642018817 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018620060 |
| oclc_num | 394927771 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG |
| owner_facet | DE-473 DE-BY-UBG |
| physical | XII, 229 S. graph. Darst. 24 cm |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spellingShingle | Taylor, Matthew E. Transfer in reinforcement learning domains Studies in computational intelligence Bestärkendes Lernen <Künstliche Intelligenz> Künstliche Intelligenz Artificial intelligence Reinforcement learning Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd |
| subject_GND | (DE-588)4825546-4 |
| title | Transfer in reinforcement learning domains |
| title_auth | Transfer in reinforcement learning domains |
| title_exact_search | Transfer in reinforcement learning domains |
| title_full | Transfer in reinforcement learning domains Matthew E. Taylor |
| title_fullStr | Transfer in reinforcement learning domains Matthew E. Taylor |
| title_full_unstemmed | Transfer in reinforcement learning domains Matthew E. Taylor |
| title_short | Transfer in reinforcement learning domains |
| title_sort | transfer in reinforcement learning domains |
| topic | Bestärkendes Lernen <Künstliche Intelligenz> Künstliche Intelligenz Artificial intelligence Reinforcement learning Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd |
| topic_facet | Bestärkendes Lernen <Künstliche Intelligenz> Künstliche Intelligenz Artificial intelligence Reinforcement learning Bestärkendes Lernen Künstliche Intelligenz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018620060&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT taylormatthewe transferinreinforcementlearningdomains |