Scaling up Mean Field Games with Online Mirror Descent Article Swipe
Julien Pérolat
,
Sarah Perrin
,
Romuald Élie
,
Mathieu Laurière
,
Georgios Piliouras
,
Matthieu Geist
,
Karl Tuyls
,
Olivier Pietquin
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.00623
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.00623
We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash equilibrium under a natural and well-motivated set of monotonicity assumptions. This theoretical result nicely extends to multi-population games and to settings involving common noise. A thorough experimental investigation on various single and multi-population MFGs shows that OMD outperforms traditional algorithms such as Fictitious Play (FP). We empirically show that OMD scales up and converges significantly faster than FP by solving, for the first time to our knowledge, examples of MFGs with hundreds of billions states. This study establishes the state-of-the-art for learning in large-scale multi-agent and multi-population games.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.00623
- https://arxiv.org/pdf/2103.00623
- OA Status
- green
- Cited By
- 10
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3133621309
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3133621309Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2103.00623Digital Object Identifier
- Title
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Scaling up Mean Field Games with Online Mirror DescentWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-02-28Full publication date if available
- Authors
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Julien Pérolat, Sarah Perrin, Romuald Élie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier PietquinList of authors in order
- Landing page
-
https://arxiv.org/abs/2103.00623Publisher landing page
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https://arxiv.org/pdf/2103.00623Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2103.00623Direct OA link when available
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Scaling, Descent (aeronautics), Field (mathematics), Computer science, Mean field theory, Statistical physics, Mathematics, Physics, Geometry, Pure mathematics, Condensed matter physics, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 1, 2022: 2, 2021: 5Per-year citation counts (last 5 years)
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40Number 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.Online | 12 |
| abstract_inverted_index.common | 48 |
| abstract_inverted_index.faster | 81 |
| abstract_inverted_index.games. | 113 |
| abstract_inverted_index.nicely | 39 |
| abstract_inverted_index.noise. | 49 |
| abstract_inverted_index.result | 38 |
| abstract_inverted_index.scales | 76 |
| abstract_inverted_index.single | 56 |
| abstract_inverted_index.Descent | 14 |
| abstract_inverted_index.address | 1 |
| abstract_inverted_index.extends | 40 |
| abstract_inverted_index.natural | 29 |
| abstract_inverted_index.scaling | 2 |
| abstract_inverted_index.states. | 100 |
| abstract_inverted_index.various | 55 |
| abstract_inverted_index.billions | 99 |
| abstract_inverted_index.examples | 93 |
| abstract_inverted_index.hundreds | 97 |
| abstract_inverted_index.learning | 107 |
| abstract_inverted_index.provably | 21 |
| abstract_inverted_index.settings | 46 |
| abstract_inverted_index.solving, | 85 |
| abstract_inverted_index.thorough | 51 |
| abstract_inverted_index.converges | 22, 79 |
| abstract_inverted_index.involving | 47 |
| abstract_inverted_index.Fictitious | 68 |
| abstract_inverted_index.algorithms | 65 |
| abstract_inverted_index.knowledge, | 92 |
| abstract_inverted_index.computation | 5 |
| abstract_inverted_index.empirically | 72 |
| abstract_inverted_index.equilibrium | 4, 26 |
| abstract_inverted_index.establishes | 103 |
| abstract_inverted_index.large-scale | 109 |
| abstract_inverted_index.multi-agent | 110 |
| abstract_inverted_index.outperforms | 63 |
| abstract_inverted_index.theoretical | 37 |
| abstract_inverted_index.traditional | 64 |
| abstract_inverted_index.assumptions. | 35 |
| abstract_inverted_index.experimental | 52 |
| abstract_inverted_index.monotonicity | 34 |
| abstract_inverted_index.investigation | 53 |
| abstract_inverted_index.significantly | 80 |
| abstract_inverted_index.well-motivated | 31 |
| abstract_inverted_index.continuous-time | 19 |
| abstract_inverted_index.multi-population | 42, 58, 112 |
| abstract_inverted_index.state-of-the-art | 105 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |