GP-ILQG: Data-driven Robust Optimal Control for Uncertain Nonlinear Dynamical Systems Article Swipe
YOU?
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· 2017
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.1705.05344
As we aim to control complex systems, use of a simulator in model-based reinforcement learning is becoming more common. However, it has been challenging to overcome the Reality Gap, which comes from nonlinear model bias and susceptibility to disturbance. To address these problems, we propose a novel algorithm that combines data-driven system identification approach (Gaussian Process) with a Differential-Dynamic-Programming-based robust optimal control method (Iterative Linear Quadratic Control). Our algorithm uses the simulator's model as the mean function for a Gaussian Process and learns only the difference between the simulator's prediction and actual observations, making it a natural hybrid of simulation and real-world observation. We show that our approach quickly corrects incorrect models, comes up with robust optimal controllers, and transfers its acquired model knowledge to new tasks efficiently.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1705.05344
- https://arxiv.org/pdf/1705.05344
- OA Status
- green
- Cited By
- 18
- References
- 25
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2615735215
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2615735215Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1705.05344Digital Object Identifier
- Title
-
GP-ILQG: Data-driven Robust Optimal Control for Uncertain Nonlinear Dynamical SystemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-05-15Full publication date if available
- Authors
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Gilwoo Lee, Siddhartha S Srinivasa, Matthew T. MasonList of authors in order
- Landing page
-
https://arxiv.org/abs/1705.05344Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1705.05344Direct 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
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https://arxiv.org/pdf/1705.05344Direct OA link when available
- Concepts
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Computer science, Reinforcement learning, Nonlinear system, Dynamic programming, Gaussian process, Differential dynamic programming, Process (computing), Control theory (sociology), Linear-quadratic-Gaussian control, Robust control, Optimal control, Quadratic programming, Gaussian, Artificial intelligence, Control (management), Mathematical optimization, Algorithm, Mathematics, Quantum mechanics, Physics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2021: 4, 2020: 4, 2019: 5, 2018: 4, 2017: 1Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.as | 73 |
| abstract_inverted_index.in | 11 |
| abstract_inverted_index.is | 15 |
| abstract_inverted_index.it | 20, 94 |
| abstract_inverted_index.of | 8, 98 |
| abstract_inverted_index.to | 3, 24, 37, 124 |
| abstract_inverted_index.up | 113 |
| abstract_inverted_index.we | 1, 43 |
| abstract_inverted_index.Our | 67 |
| abstract_inverted_index.aim | 2 |
| abstract_inverted_index.and | 35, 81, 90, 100, 118 |
| abstract_inverted_index.for | 77 |
| abstract_inverted_index.has | 21 |
| abstract_inverted_index.its | 120 |
| abstract_inverted_index.new | 125 |
| abstract_inverted_index.our | 106 |
| abstract_inverted_index.the | 26, 70, 74, 84, 87 |
| abstract_inverted_index.use | 7 |
| abstract_inverted_index.Gap, | 28 |
| abstract_inverted_index.been | 22 |
| abstract_inverted_index.bias | 34 |
| abstract_inverted_index.from | 31 |
| abstract_inverted_index.mean | 75 |
| abstract_inverted_index.more | 17 |
| abstract_inverted_index.only | 83 |
| abstract_inverted_index.show | 104 |
| abstract_inverted_index.that | 48, 105 |
| abstract_inverted_index.uses | 69 |
| abstract_inverted_index.with | 56, 114 |
| abstract_inverted_index.comes | 30, 112 |
| abstract_inverted_index.model | 33, 72, 122 |
| abstract_inverted_index.novel | 46 |
| abstract_inverted_index.tasks | 126 |
| abstract_inverted_index.these | 41 |
| abstract_inverted_index.which | 29 |
| abstract_inverted_index.Linear | 64 |
| abstract_inverted_index.actual | 91 |
| abstract_inverted_index.hybrid | 97 |
| abstract_inverted_index.learns | 82 |
| abstract_inverted_index.making | 93 |
| abstract_inverted_index.method | 62 |
| abstract_inverted_index.robust | 59, 115 |
| abstract_inverted_index.system | 51 |
| abstract_inverted_index.Process | 80 |
| abstract_inverted_index.Reality | 27 |
| abstract_inverted_index.address | 40 |
| abstract_inverted_index.between | 86 |
| abstract_inverted_index.common. | 18 |
| abstract_inverted_index.complex | 5 |
| abstract_inverted_index.control | 4, 61 |
| abstract_inverted_index.models, | 111 |
| abstract_inverted_index.natural | 96 |
| abstract_inverted_index.optimal | 60, 116 |
| abstract_inverted_index.propose | 44 |
| abstract_inverted_index.quickly | 108 |
| abstract_inverted_index.Gaussian | 79 |
| abstract_inverted_index.However, | 19 |
| abstract_inverted_index.Process) | 55 |
| abstract_inverted_index.acquired | 121 |
| abstract_inverted_index.approach | 53, 107 |
| abstract_inverted_index.becoming | 16 |
| abstract_inverted_index.combines | 49 |
| abstract_inverted_index.corrects | 109 |
| abstract_inverted_index.function | 76 |
| abstract_inverted_index.learning | 14 |
| abstract_inverted_index.overcome | 25 |
| abstract_inverted_index.systems, | 6 |
| abstract_inverted_index.(Gaussian | 54 |
| abstract_inverted_index.Control). | 66 |
| abstract_inverted_index.Quadratic | 65 |
| abstract_inverted_index.algorithm | 47, 68 |
| abstract_inverted_index.incorrect | 110 |
| abstract_inverted_index.knowledge | 123 |
| abstract_inverted_index.nonlinear | 32 |
| abstract_inverted_index.problems, | 42 |
| abstract_inverted_index.simulator | 10 |
| abstract_inverted_index.transfers | 119 |
| abstract_inverted_index.(Iterative | 63 |
| abstract_inverted_index.difference | 85 |
| abstract_inverted_index.prediction | 89 |
| abstract_inverted_index.real-world | 101 |
| abstract_inverted_index.simulation | 99 |
| abstract_inverted_index.challenging | 23 |
| abstract_inverted_index.data-driven | 50 |
| abstract_inverted_index.model-based | 12 |
| abstract_inverted_index.simulator's | 71, 88 |
| abstract_inverted_index.controllers, | 117 |
| abstract_inverted_index.disturbance. | 38 |
| abstract_inverted_index.efficiently. | 127 |
| abstract_inverted_index.observation. | 102 |
| abstract_inverted_index.observations, | 92 |
| abstract_inverted_index.reinforcement | 13 |
| abstract_inverted_index.identification | 52 |
| abstract_inverted_index.susceptibility | 36 |
| abstract_inverted_index.Differential-Dynamic-Programming-based | 58 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |