Continual Auxiliary Task Learning Article Swipe
Matthew J. McLeod
,
Chunlok Lo
,
Matthew Schlegel
,
Andrew Jacobsen
,
Raksha Kumaraswamy
,
Martha White
,
Adam White
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.11133
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.11133
Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems. A variety of off-policy learning algorithms have been developed to learn such predictions, but as yet there is little work on how to adapt the behavior to gather useful data for those off-policy predictions. In this work, we investigate a reinforcement learning system designed to learn a collection of auxiliary tasks, with a behavior policy learning to take actions to improve those auxiliary predictions. We highlight the inherent non-stationarity in this continual auxiliary task learning problem, for both prediction learners and the behavior learner. We develop an algorithm based on successor features that facilitates tracking under non-stationary rewards, and prove the separation into learning successor features and rewards provides convergence rate improvements. We conduct an in-depth study into the resulting multi-prediction learning system.
Related Topics
Concepts
Successor cardinal
Reinforcement learning
Computer science
Task (project management)
Artificial intelligence
Machine learning
Variety (cybernetics)
Convergence (economics)
Multi-task learning
Error-driven learning
Policy learning
Active learning (machine learning)
Work (physics)
Engineering
Mathematics
Economics
Mathematical analysis
Systems engineering
Mechanical engineering
Economic growth
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2202.11133
- https://arxiv.org/pdf/2202.11133
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225554067
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4225554067Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2202.11133Digital Object Identifier
- Title
-
Continual Auxiliary Task LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-22Full publication date if available
- Authors
-
Matthew J. McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam WhiteList of authors in order
- Landing page
-
https://arxiv.org/abs/2202.11133Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2202.11133Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2202.11133Direct OA link when available
- Concepts
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Successor cardinal, Reinforcement learning, Computer science, Task (project management), Artificial intelligence, Machine learning, Variety (cybernetics), Convergence (economics), Multi-task learning, Error-driven learning, Policy learning, Active learning (machine learning), Work (physics), Engineering, Mathematics, Economics, Mathematical analysis, Systems engineering, Mechanical engineering, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.tasks, | 2, 68 |
| abstract_inverted_index.useful | 46 |
| abstract_inverted_index.world, | 9 |
| abstract_inverted_index.actions | 76 |
| abstract_inverted_index.conduct | 131 |
| abstract_inverted_index.develop | 103 |
| abstract_inverted_index.improve | 78 |
| abstract_inverted_index.provide | 11 |
| abstract_inverted_index.rewards | 125 |
| abstract_inverted_index.system. | 140 |
| abstract_inverted_index.variety | 19 |
| abstract_inverted_index.Learning | 0 |
| abstract_inverted_index.behavior | 43, 71, 100 |
| abstract_inverted_index.benefits | 13 |
| abstract_inverted_index.designed | 61 |
| abstract_inverted_index.features | 109, 123 |
| abstract_inverted_index.in-depth | 133 |
| abstract_inverted_index.inherent | 85 |
| abstract_inverted_index.learner. | 101 |
| abstract_inverted_index.learners | 97 |
| abstract_inverted_index.learning | 16, 22, 59, 73, 92, 121, 139 |
| abstract_inverted_index.multiple | 5 |
| abstract_inverted_index.problem, | 93 |
| abstract_inverted_index.provides | 126 |
| abstract_inverted_index.rewards, | 115 |
| abstract_inverted_index.systems. | 17 |
| abstract_inverted_index.tracking | 112 |
| abstract_inverted_index.algorithm | 105 |
| abstract_inverted_index.auxiliary | 1, 67, 80, 90 |
| abstract_inverted_index.continual | 89 |
| abstract_inverted_index.developed | 26 |
| abstract_inverted_index.highlight | 83 |
| abstract_inverted_index.resulting | 137 |
| abstract_inverted_index.successor | 108, 122 |
| abstract_inverted_index.algorithms | 23 |
| abstract_inverted_index.collection | 65 |
| abstract_inverted_index.off-policy | 21, 50 |
| abstract_inverted_index.prediction | 96 |
| abstract_inverted_index.separation | 119 |
| abstract_inverted_index.convergence | 127 |
| abstract_inverted_index.facilitates | 111 |
| abstract_inverted_index.investigate | 56 |
| abstract_inverted_index.predictions | 6 |
| abstract_inverted_index.predictions, | 30 |
| abstract_inverted_index.predictions. | 51, 81 |
| abstract_inverted_index.improvements. | 129 |
| abstract_inverted_index.reinforcement | 15, 58 |
| abstract_inverted_index.non-stationary | 114 |
| abstract_inverted_index.multi-prediction | 138 |
| abstract_inverted_index.non-stationarity | 86 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |