Pretty darn good control: when are approximate solutions better than approximate models Article Swipe
Felipe Montealegre‐Mora
,
Marcus Lapeyrolerie
,
Melissa Chapman
,
Abigail G. Keller
,
Carl Boettiger
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.13654
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.13654
Existing methods for optimal control struggle to deal with the complexity commonly encountered in real-world systems, including dimensionality, process error, model bias and data heterogeneity. Instead of tackling these system complexities directly, researchers have typically sought to simplify models to fit optimal control methods. But when is the optimal solution to an approximate, stylized model better than an approximate solution to a more accurate model? While this question has largely gone unanswered owing to the difficulty of finding even approximate solutions for complex models, recent algorithmic and computational advances in deep reinforcement learning (DRL) might finally allow us to address these questions. DRL methods have to date been applied primarily in the context of games or robotic mechanics, which operate under precisely known rules. Here, we demonstrate the ability for DRL algorithms using deep neural networks to successfully approximate solutions (the "policy function" or control rule) in a non-linear three-variable model for a fishery without knowing or ever attempting to infer a model for the process itself. We find that the reinforcement learning agent discovers an effective simplification of the problem to obtain an interpretable control rule. We show that the policy obtained with DRL is both more profitable and more sustainable than any constant mortality policy -- the standard family of policies considered in fishery management.
Related Topics
Concepts
Reinforcement learning
Stylized fact
Context (archaeology)
Computer science
Curse of dimensionality
Process (computing)
Control (management)
Artificial intelligence
Artificial neural network
Function (biology)
Mathematical optimization
Optimal control
Function approximation
Mathematics
Economics
Operating system
Biology
Paleontology
Macroeconomics
Evolutionary biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.13654
- https://arxiv.org/pdf/2308.13654
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386269501
All OpenAlex metadata
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- DOI
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https://doi.org/10.48550/arxiv.2308.13654Digital Object Identifier
- Title
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Pretty darn good control: when are approximate solutions better than approximate modelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-25Full publication date if available
- Authors
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Felipe Montealegre‐Mora, Marcus Lapeyrolerie, Melissa Chapman, Abigail G. Keller, Carl BoettigerList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.13654Publisher landing page
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https://arxiv.org/pdf/2308.13654Direct 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/2308.13654Direct OA link when available
- Concepts
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Reinforcement learning, Stylized fact, Context (archaeology), Computer science, Curse of dimensionality, Process (computing), Control (management), Artificial intelligence, Artificial neural network, Function (biology), Mathematical optimization, Optimal control, Function approximation, Mathematics, Economics, Operating system, Biology, Paleontology, Macroeconomics, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.these | 28, 100 |
| abstract_inverted_index.under | 120 |
| abstract_inverted_index.using | 132 |
| abstract_inverted_index.which | 118 |
| abstract_inverted_index.better | 55 |
| abstract_inverted_index.error, | 19 |
| abstract_inverted_index.family | 210 |
| abstract_inverted_index.model? | 64 |
| abstract_inverted_index.models | 38 |
| abstract_inverted_index.neural | 134 |
| abstract_inverted_index.obtain | 182 |
| abstract_inverted_index.policy | 191, 206 |
| abstract_inverted_index.recent | 84 |
| abstract_inverted_index.rules. | 123 |
| abstract_inverted_index.sought | 35 |
| abstract_inverted_index.system | 29 |
| abstract_inverted_index."policy | 141 |
| abstract_inverted_index.Instead | 25 |
| abstract_inverted_index.ability | 128 |
| abstract_inverted_index.address | 99 |
| abstract_inverted_index.applied | 108 |
| abstract_inverted_index.complex | 82 |
| abstract_inverted_index.context | 112 |
| abstract_inverted_index.control | 4, 42, 144, 185 |
| abstract_inverted_index.finally | 95 |
| abstract_inverted_index.finding | 77 |
| abstract_inverted_index.fishery | 153, 215 |
| abstract_inverted_index.itself. | 166 |
| abstract_inverted_index.knowing | 155 |
| abstract_inverted_index.largely | 69 |
| abstract_inverted_index.methods | 1, 103 |
| abstract_inverted_index.models, | 83 |
| abstract_inverted_index.operate | 119 |
| abstract_inverted_index.optimal | 3, 41, 48 |
| abstract_inverted_index.problem | 180 |
| abstract_inverted_index.process | 18, 165 |
| abstract_inverted_index.robotic | 116 |
| abstract_inverted_index.without | 154 |
| abstract_inverted_index.Existing | 0 |
| abstract_inverted_index.accurate | 63 |
| abstract_inverted_index.advances | 88 |
| abstract_inverted_index.commonly | 11 |
| abstract_inverted_index.constant | 204 |
| abstract_inverted_index.learning | 92, 172 |
| abstract_inverted_index.methods. | 43 |
| abstract_inverted_index.networks | 135 |
| abstract_inverted_index.obtained | 192 |
| abstract_inverted_index.policies | 212 |
| abstract_inverted_index.question | 67 |
| abstract_inverted_index.simplify | 37 |
| abstract_inverted_index.solution | 49, 59 |
| abstract_inverted_index.standard | 209 |
| abstract_inverted_index.struggle | 5 |
| abstract_inverted_index.stylized | 53 |
| abstract_inverted_index.systems, | 15 |
| abstract_inverted_index.tackling | 27 |
| abstract_inverted_index.directly, | 31 |
| abstract_inverted_index.discovers | 174 |
| abstract_inverted_index.effective | 176 |
| abstract_inverted_index.function" | 142 |
| abstract_inverted_index.including | 16 |
| abstract_inverted_index.mortality | 205 |
| abstract_inverted_index.precisely | 121 |
| abstract_inverted_index.primarily | 109 |
| abstract_inverted_index.solutions | 80, 139 |
| abstract_inverted_index.typically | 34 |
| abstract_inverted_index.algorithms | 131 |
| abstract_inverted_index.attempting | 158 |
| abstract_inverted_index.complexity | 10 |
| abstract_inverted_index.considered | 213 |
| abstract_inverted_index.difficulty | 75 |
| abstract_inverted_index.mechanics, | 117 |
| abstract_inverted_index.non-linear | 148 |
| abstract_inverted_index.profitable | 198 |
| abstract_inverted_index.questions. | 101 |
| abstract_inverted_index.real-world | 14 |
| abstract_inverted_index.unanswered | 71 |
| abstract_inverted_index.algorithmic | 85 |
| abstract_inverted_index.approximate | 58, 79, 138 |
| abstract_inverted_index.demonstrate | 126 |
| abstract_inverted_index.encountered | 12 |
| abstract_inverted_index.management. | 216 |
| abstract_inverted_index.researchers | 32 |
| abstract_inverted_index.sustainable | 201 |
| abstract_inverted_index.approximate, | 52 |
| abstract_inverted_index.complexities | 30 |
| abstract_inverted_index.successfully | 137 |
| abstract_inverted_index.computational | 87 |
| abstract_inverted_index.interpretable | 184 |
| abstract_inverted_index.reinforcement | 91, 171 |
| abstract_inverted_index.heterogeneity. | 24 |
| abstract_inverted_index.simplification | 177 |
| abstract_inverted_index.three-variable | 149 |
| abstract_inverted_index.dimensionality, | 17 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.12328189 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |