Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand Article Swipe
Daniele Gammelli
,
Kaidi Yang
,
J. Michael Harrison
,
Filipe Rodrigues
,
Francisco C. Pereira
,
Marco Pavone
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3534678.3539180
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3534678.3539180
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs. By centrally controlling a fleet of self-driving vehicles, these systems provide mobility service to customers and are currently starting to be deployed in a number of cities around the world. Current learning-based approaches for controlling AMoD systems are limited to the single-city scenario, whereby the service operator is allowed to take an unlimited amount of operational decisions within the same transportation system. However, real-world system operators can hardly afford to fully re-train AMoD controllers for every city they operate in, as this could result in a high number of poor-quality decisions during training, making the single-city strategy a potentially impractical solution. To address these limitations, we propose to formalize the multi-city AMoD problem through the lens of meta-reinforcement learning (meta-RL) and devise an actor-critic algorithm based on recurrent graph neural networks. In our approach, AMoD controllers are explicitly trained such that a small amount of experience within a new city will produce good system performance. Empirically, we show how control policies learned through meta-RL are able to achieve near-optimal performance on unseen cities by learning rapidly adaptable policies, thus making them more robust not only to novel environments, but also to distribution shifts common in real-world operations, such as special events, unexpected congestion, and dynamic pricing schemes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3534678.3539180
- https://dl.acm.org/doi/pdf/10.1145/3534678.3539180
- OA Status
- hybrid
- Cited By
- 17
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4290943640Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3534678.3539180Digital Object Identifier
- Title
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Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-DemandWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-08-12Full publication date if available
- Authors
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Daniele Gammelli, Kaidi Yang, J. Michael Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco PavoneList of authors in order
- Landing page
-
https://doi.org/10.1145/3534678.3539180Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3534678.3539180Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3534678.3539180Direct OA link when available
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Reinforcement learning, Computer science, Service (business), Operations research, Artificial intelligence, Engineering, Economics, EconomyTop concepts (fields/topics) attached by OpenAlex
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17Total citation count in OpenAlex
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2025: 1, 2024: 12, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.our | 152 |
| abstract_inverted_index.the | 48, 60, 64, 78, 114, 129, 134 |
| abstract_inverted_index.AMoD | 55, 92, 131, 154 |
| abstract_inverted_index.able | 185 |
| abstract_inverted_index.also | 209 |
| abstract_inverted_index.city | 96, 169 |
| abstract_inverted_index.good | 172 |
| abstract_inverted_index.high | 106 |
| abstract_inverted_index.lens | 135 |
| abstract_inverted_index.more | 201 |
| abstract_inverted_index.only | 204 |
| abstract_inverted_index.same | 79 |
| abstract_inverted_index.show | 177 |
| abstract_inverted_index.such | 159, 217 |
| abstract_inverted_index.take | 70 |
| abstract_inverted_index.that | 160 |
| abstract_inverted_index.them | 200 |
| abstract_inverted_index.they | 97 |
| abstract_inverted_index.this | 101 |
| abstract_inverted_index.thus | 198 |
| abstract_inverted_index.will | 170 |
| abstract_inverted_index.based | 145 |
| abstract_inverted_index.could | 102 |
| abstract_inverted_index.every | 95 |
| abstract_inverted_index.fleet | 24 |
| abstract_inverted_index.fully | 90 |
| abstract_inverted_index.graph | 148 |
| abstract_inverted_index.novel | 206 |
| abstract_inverted_index.small | 162 |
| abstract_inverted_index.these | 28, 123 |
| abstract_inverted_index.(AMoD) | 2 |
| abstract_inverted_index.afford | 88 |
| abstract_inverted_index.amount | 73, 163 |
| abstract_inverted_index.around | 47 |
| abstract_inverted_index.cities | 46, 192 |
| abstract_inverted_index.common | 213 |
| abstract_inverted_index.devise | 141 |
| abstract_inverted_index.during | 111 |
| abstract_inverted_index.hardly | 87 |
| abstract_inverted_index.making | 113, 199 |
| abstract_inverted_index.needs. | 19 |
| abstract_inverted_index.neural | 149 |
| abstract_inverted_index.number | 44, 107 |
| abstract_inverted_index.result | 103 |
| abstract_inverted_index.robust | 202 |
| abstract_inverted_index.shifts | 212 |
| abstract_inverted_index.system | 84, 173 |
| abstract_inverted_index.travel | 18 |
| abstract_inverted_index.unseen | 191 |
| abstract_inverted_index.within | 77, 166 |
| abstract_inverted_index.world. | 49 |
| abstract_inverted_index.Current | 50 |
| abstract_inverted_index.achieve | 187 |
| abstract_inverted_index.address | 122 |
| abstract_inverted_index.allowed | 68 |
| abstract_inverted_index.control | 179 |
| abstract_inverted_index.dynamic | 224 |
| abstract_inverted_index.events, | 220 |
| abstract_inverted_index.learned | 181 |
| abstract_inverted_index.limited | 58 |
| abstract_inverted_index.meta-RL | 183 |
| abstract_inverted_index.operate | 98 |
| abstract_inverted_index.pricing | 225 |
| abstract_inverted_index.problem | 132 |
| abstract_inverted_index.produce | 171 |
| abstract_inverted_index.propose | 126 |
| abstract_inverted_index.provide | 30 |
| abstract_inverted_index.rapidly | 195 |
| abstract_inverted_index.service | 32, 65 |
| abstract_inverted_index.special | 219 |
| abstract_inverted_index.system. | 81 |
| abstract_inverted_index.systems | 3, 29, 56 |
| abstract_inverted_index.through | 133, 182 |
| abstract_inverted_index.trained | 158 |
| abstract_inverted_index.whereby | 63 |
| abstract_inverted_index.However, | 82 |
| abstract_inverted_index.deployed | 41 |
| abstract_inverted_index.existing | 9 |
| abstract_inverted_index.learning | 138, 194 |
| abstract_inverted_index.mobility | 31 |
| abstract_inverted_index.operator | 66 |
| abstract_inverted_index.policies | 180 |
| abstract_inverted_index.re-train | 91 |
| abstract_inverted_index.schemes. | 226 |
| abstract_inverted_index.starting | 38 |
| abstract_inverted_index.strategy | 116 |
| abstract_inverted_index.(meta-RL) | 139 |
| abstract_inverted_index.adaptable | 196 |
| abstract_inverted_index.algorithm | 144 |
| abstract_inverted_index.approach, | 153 |
| abstract_inverted_index.centrally | 21 |
| abstract_inverted_index.currently | 12, 37 |
| abstract_inverted_index.customers | 34 |
| abstract_inverted_index.decisions | 76, 110 |
| abstract_inverted_index.formalize | 128 |
| abstract_inverted_index.networks. | 150 |
| abstract_inverted_index.operators | 85 |
| abstract_inverted_index.policies, | 197 |
| abstract_inverted_index.recurrent | 147 |
| abstract_inverted_index.represent | 4 |
| abstract_inverted_index.scenario, | 62 |
| abstract_inverted_index.solution. | 120 |
| abstract_inverted_index.training, | 112 |
| abstract_inverted_index.unlimited | 72 |
| abstract_inverted_index.vehicles, | 27 |
| abstract_inverted_index.Autonomous | 0 |
| abstract_inverted_index.approaches | 52 |
| abstract_inverted_index.attractive | 6 |
| abstract_inverted_index.challenged | 13 |
| abstract_inverted_index.experience | 165 |
| abstract_inverted_index.explicitly | 157 |
| abstract_inverted_index.increasing | 17 |
| abstract_inverted_index.multi-city | 130 |
| abstract_inverted_index.paradigms, | 11 |
| abstract_inverted_index.real-world | 83, 215 |
| abstract_inverted_index.unexpected | 221 |
| abstract_inverted_index.alternative | 7 |
| abstract_inverted_index.congestion, | 222 |
| abstract_inverted_index.controllers | 93, 155 |
| abstract_inverted_index.controlling | 22, 54 |
| abstract_inverted_index.impractical | 119 |
| abstract_inverted_index.operational | 75 |
| abstract_inverted_index.operations, | 216 |
| abstract_inverted_index.performance | 189 |
| abstract_inverted_index.potentially | 118 |
| abstract_inverted_index.single-city | 61, 115 |
| abstract_inverted_index.Empirically, | 175 |
| abstract_inverted_index.actor-critic | 143 |
| abstract_inverted_index.distribution | 211 |
| abstract_inverted_index.limitations, | 124 |
| abstract_inverted_index.near-optimal | 188 |
| abstract_inverted_index.performance. | 174 |
| abstract_inverted_index.poor-quality | 109 |
| abstract_inverted_index.self-driving | 26 |
| abstract_inverted_index.urbanization | 15 |
| abstract_inverted_index.environments, | 207 |
| abstract_inverted_index.learning-based | 51 |
| abstract_inverted_index.transportation | 10, 80 |
| abstract_inverted_index.Mobility-on-Demand | 1 |
| abstract_inverted_index.meta-reinforcement | 137 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.96147621 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |