The Utility of Sparse Representations for Control in Reinforcement Learning Article Swipe
Vincent Liu
,
Raksha Kumaraswamy
,
Lei Le
,
Martha White
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1811.06626
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1811.06626
We investigate sparse representations for control in reinforcement learning. While these representations are widely used in computer vision, their prevalence in reinforcement learning is limited to sparse coding where extracting representations for new data can be computationally intensive. Here, we begin by demonstrating that learning a control policy incrementally with a representation from a standard neural network fails in classic control domains, whereas learning with a representation obtained from a neural network that has sparsity properties enforced is effective. We provide evidence that the reason for this is that the sparse representation provides locality, and so avoids catastrophic interference, and particularly keeps consistent, stable values for bootstrapping. We then discuss how to learn such sparse representations. We explore the idea of Distributional Regularizers, where the activation of hidden nodes is encouraged to match a particular distribution that results in sparse activation across time. We identify a simple but effective way to obtain sparse representations, not afforded by previously proposed strategies, making it more practical for further investigation into sparse representations for reinforcement learning.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1811.06626
- https://arxiv.org/pdf/1811.06626
- OA Status
- green
- Cited By
- 9
- References
- 29
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W2900757704Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1811.06626Digital Object Identifier
- Title
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The Utility of Sparse Representations for Control in Reinforcement LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-11-15Full publication date if available
- Authors
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Vincent Liu, Raksha Kumaraswamy, Lei Le, Martha WhiteList of authors in order
- Landing page
-
https://arxiv.org/abs/1811.06626Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1811.06626Direct 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
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https://arxiv.org/pdf/1811.06626Direct OA link when available
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Reinforcement learning, Computer science, Bootstrapping (finance), Neural coding, Sparse approximation, Artificial intelligence, Representation (politics), Machine learning, Locality, Artificial neural network, Mathematics, Law, Philosophy, Linguistics, Political science, Econometrics, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2021: 2, 2020: 3, 2019: 3Per-year citation counts (last 5 years)
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29Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.and | 94, 99 |
| abstract_inverted_index.are | 12 |
| abstract_inverted_index.but | 147 |
| abstract_inverted_index.can | 34 |
| abstract_inverted_index.for | 4, 31, 85, 105, 164, 170 |
| abstract_inverted_index.has | 73 |
| abstract_inverted_index.how | 110 |
| abstract_inverted_index.new | 32 |
| abstract_inverted_index.not | 154 |
| abstract_inverted_index.the | 83, 89, 118, 124 |
| abstract_inverted_index.way | 149 |
| abstract_inverted_index.data | 33 |
| abstract_inverted_index.from | 52, 68 |
| abstract_inverted_index.idea | 119 |
| abstract_inverted_index.into | 167 |
| abstract_inverted_index.more | 162 |
| abstract_inverted_index.such | 113 |
| abstract_inverted_index.that | 43, 72, 82, 88, 136 |
| abstract_inverted_index.then | 108 |
| abstract_inverted_index.this | 86 |
| abstract_inverted_index.used | 14 |
| abstract_inverted_index.with | 49, 64 |
| abstract_inverted_index.Here, | 38 |
| abstract_inverted_index.While | 9 |
| abstract_inverted_index.begin | 40 |
| abstract_inverted_index.fails | 57 |
| abstract_inverted_index.keeps | 101 |
| abstract_inverted_index.learn | 112 |
| abstract_inverted_index.match | 132 |
| abstract_inverted_index.nodes | 128 |
| abstract_inverted_index.their | 18 |
| abstract_inverted_index.these | 10 |
| abstract_inverted_index.time. | 142 |
| abstract_inverted_index.where | 28, 123 |
| abstract_inverted_index.across | 141 |
| abstract_inverted_index.avoids | 96 |
| abstract_inverted_index.coding | 27 |
| abstract_inverted_index.hidden | 127 |
| abstract_inverted_index.making | 160 |
| abstract_inverted_index.neural | 55, 70 |
| abstract_inverted_index.obtain | 151 |
| abstract_inverted_index.policy | 47 |
| abstract_inverted_index.reason | 84 |
| abstract_inverted_index.simple | 146 |
| abstract_inverted_index.sparse | 2, 26, 90, 114, 139, 152, 168 |
| abstract_inverted_index.stable | 103 |
| abstract_inverted_index.values | 104 |
| abstract_inverted_index.widely | 13 |
| abstract_inverted_index.classic | 59 |
| abstract_inverted_index.control | 5, 46, 60 |
| abstract_inverted_index.discuss | 109 |
| abstract_inverted_index.explore | 117 |
| abstract_inverted_index.further | 165 |
| abstract_inverted_index.limited | 24 |
| abstract_inverted_index.network | 56, 71 |
| abstract_inverted_index.provide | 80 |
| abstract_inverted_index.results | 137 |
| abstract_inverted_index.vision, | 17 |
| abstract_inverted_index.whereas | 62 |
| abstract_inverted_index.afforded | 155 |
| abstract_inverted_index.computer | 16 |
| abstract_inverted_index.domains, | 61 |
| abstract_inverted_index.enforced | 76 |
| abstract_inverted_index.evidence | 81 |
| abstract_inverted_index.identify | 144 |
| abstract_inverted_index.learning | 22, 44, 63 |
| abstract_inverted_index.obtained | 67 |
| abstract_inverted_index.proposed | 158 |
| abstract_inverted_index.provides | 92 |
| abstract_inverted_index.sparsity | 74 |
| abstract_inverted_index.standard | 54 |
| abstract_inverted_index.effective | 148 |
| abstract_inverted_index.learning. | 8, 172 |
| abstract_inverted_index.locality, | 93 |
| abstract_inverted_index.practical | 163 |
| abstract_inverted_index.activation | 125, 140 |
| abstract_inverted_index.effective. | 78 |
| abstract_inverted_index.encouraged | 130 |
| abstract_inverted_index.extracting | 29 |
| abstract_inverted_index.intensive. | 37 |
| abstract_inverted_index.particular | 134 |
| abstract_inverted_index.prevalence | 19 |
| abstract_inverted_index.previously | 157 |
| abstract_inverted_index.properties | 75 |
| abstract_inverted_index.consistent, | 102 |
| abstract_inverted_index.investigate | 1 |
| abstract_inverted_index.strategies, | 159 |
| abstract_inverted_index.catastrophic | 97 |
| abstract_inverted_index.distribution | 135 |
| abstract_inverted_index.particularly | 100 |
| abstract_inverted_index.Regularizers, | 122 |
| abstract_inverted_index.demonstrating | 42 |
| abstract_inverted_index.incrementally | 48 |
| abstract_inverted_index.interference, | 98 |
| abstract_inverted_index.investigation | 166 |
| abstract_inverted_index.reinforcement | 7, 21, 171 |
| abstract_inverted_index.Distributional | 121 |
| abstract_inverted_index.bootstrapping. | 106 |
| abstract_inverted_index.representation | 51, 66, 91 |
| abstract_inverted_index.computationally | 36 |
| abstract_inverted_index.representations | 3, 11, 30, 169 |
| abstract_inverted_index.representations, | 153 |
| abstract_inverted_index.representations. | 115 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |