Reinforcement Learning, Bit by Bit Article Swipe
Xiuyuan Lu
,
Benjamin Van Roy
,
Vikranth Dwaracherla
,
Morteza Ibrahimi
,
Ian Osband
,
Zheng Wen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.04047
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.04047
Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency poses an impediment to carrying this success over to real environments. The design of data-efficient agents calls for a deeper understanding of information acquisition and representation. We discuss concepts and regret analysis that together offer principled guidance. This line of thinking sheds light on questions of what information to seek, how to seek that information, and what information to retain. To illustrate concepts, we design simple agents that build on them and present computational results that highlight data efficiency.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.04047
- https://arxiv.org/pdf/2103.04047
- OA Status
- green
- Cited By
- 5
- References
- 81
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3134238439
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3134238439Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2103.04047Digital Object Identifier
- Title
-
Reinforcement Learning, Bit by BitWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-03-06Full publication date if available
- Authors
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Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng WenList of authors in order
- Landing page
-
https://arxiv.org/abs/2103.04047Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2103.04047Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2103.04047Direct OA link when available
- Concepts
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Regret, Reinforcement learning, Computer science, Representation (politics), Simple (philosophy), Bit (key), Human–computer interaction, Reinforcement, Artificial intelligence, Data science, Machine learning, Psychology, Computer security, Social psychology, Philosophy, Political science, Politics, Law, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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81Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.an | 13 |
| abstract_inverted_index.in | 7 |
| abstract_inverted_index.of | 25, 33, 51, 57 |
| abstract_inverted_index.on | 55, 81 |
| abstract_inverted_index.to | 15, 20, 60, 63, 70 |
| abstract_inverted_index.we | 75 |
| abstract_inverted_index.The | 23 |
| abstract_inverted_index.and | 36, 41, 67, 83 |
| abstract_inverted_index.for | 29 |
| abstract_inverted_index.how | 62 |
| abstract_inverted_index.Data | 10 |
| abstract_inverted_index.This | 49 |
| abstract_inverted_index.data | 89 |
| abstract_inverted_index.have | 3 |
| abstract_inverted_index.line | 50 |
| abstract_inverted_index.over | 19 |
| abstract_inverted_index.real | 21 |
| abstract_inverted_index.seek | 64 |
| abstract_inverted_index.that | 44, 65, 79, 87 |
| abstract_inverted_index.them | 82 |
| abstract_inverted_index.this | 17 |
| abstract_inverted_index.what | 58, 68 |
| abstract_inverted_index.build | 80 |
| abstract_inverted_index.calls | 28 |
| abstract_inverted_index.light | 54 |
| abstract_inverted_index.offer | 46 |
| abstract_inverted_index.poses | 12 |
| abstract_inverted_index.seek, | 61 |
| abstract_inverted_index.sheds | 53 |
| abstract_inverted_index.agents | 2, 27, 78 |
| abstract_inverted_index.deeper | 31 |
| abstract_inverted_index.design | 24, 76 |
| abstract_inverted_index.regret | 42 |
| abstract_inverted_index.simple | 77 |
| abstract_inverted_index.discuss | 39 |
| abstract_inverted_index.present | 84 |
| abstract_inverted_index.results | 86 |
| abstract_inverted_index.retain. | 71 |
| abstract_inverted_index.success | 18 |
| abstract_inverted_index.analysis | 43 |
| abstract_inverted_index.carrying | 16 |
| abstract_inverted_index.concepts | 40 |
| abstract_inverted_index.learning | 1 |
| abstract_inverted_index.thinking | 52 |
| abstract_inverted_index.together | 45 |
| abstract_inverted_index.concepts, | 74 |
| abstract_inverted_index.guidance. | 48 |
| abstract_inverted_index.highlight | 88 |
| abstract_inverted_index.questions | 56 |
| abstract_inverted_index.simulated | 8 |
| abstract_inverted_index.efficiency | 11 |
| abstract_inverted_index.illustrate | 73 |
| abstract_inverted_index.impediment | 14 |
| abstract_inverted_index.principled | 47 |
| abstract_inverted_index.remarkable | 5 |
| abstract_inverted_index.acquisition | 35 |
| abstract_inverted_index.efficiency. | 90 |
| abstract_inverted_index.information | 34, 59, 69 |
| abstract_inverted_index.achievements | 6 |
| abstract_inverted_index.demonstrated | 4 |
| abstract_inverted_index.information, | 66 |
| abstract_inverted_index.Reinforcement | 0 |
| abstract_inverted_index.computational | 85 |
| abstract_inverted_index.environments. | 9, 22 |
| abstract_inverted_index.understanding | 32 |
| abstract_inverted_index.data-efficient | 26 |
| abstract_inverted_index.representation. | 37 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |