On the Convergence of Model Free Learning in Mean Field Games Article Swipe
Romuald Élie
,
Julien Pérolat
,
Mathieu Laurière
,
Matthieu Geist
,
Olivier Pietquin
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v34i05.6203
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v34i05.6203
Learning by experience in Multi-Agent Systems (MAS) is a difficult and exciting task, due to the lack of stationarity of the environment, whose dynamics evolves as the population learns. In order to design scalable algorithms for systems with a large population of interacting agents (e.g., swarms), this paper focuses on Mean Field MAS, where the number of agents is asymptotically infinite. Recently, a very active burgeoning field studies the effects of diverse reinforcement learning algorithms for agents with no prior information on a stationary Mean Field Game (MFG) and learn their policy through repeated experience. We adopt a high perspective on this problem and analyze in full generality the convergence of a fictitious iterative scheme using any single agent learning algorithm at each step. We quantify the quality of the computed approximate Nash equilibrium, in terms of the accumulated errors arising at each learning iteration step. Notably, we show for the first time convergence of model free learning algorithms towards non-stationary MFG equilibria, relying only on classical assumptions on the MFG dynamics. We illustrate our theoretical results with a numerical experiment in a continuous action-space environment, where the approximate best response of the iterative fictitious play scheme is computed with a deep RL algorithm.
Related Topics
Concepts
Reinforcement learning
Convergence (economics)
Generality
Computer science
Fictitious play
Population
Nash equilibrium
Mathematical optimization
Field (mathematics)
Scalability
Iterative learning control
Artificial intelligence
Mathematics
Database
Control (management)
Psychology
Psychotherapist
Sociology
Economics
Pure mathematics
Demography
Economic growth
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v34i05.6203
- https://ojs.aaai.org/index.php/AAAI/article/download/6203/6059
- OA Status
- diamond
- Cited By
- 1
- References
- 29
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2995185709
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2995185709Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1609/aaai.v34i05.6203Digital Object Identifier
- Title
-
On the Convergence of Model Free Learning in Mean Field GamesWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-04-03Full publication date if available
- Authors
-
Romuald Élie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier PietquinList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v34i05.6203Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/6203/6059Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ojs.aaai.org/index.php/AAAI/article/download/6203/6059Direct OA link when available
- Concepts
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Reinforcement learning, Convergence (economics), Generality, Computer science, Fictitious play, Population, Nash equilibrium, Mathematical optimization, Field (mathematics), Scalability, Iterative learning control, Artificial intelligence, Mathematics, Database, Control (management), Psychology, Psychotherapist, Sociology, Economics, Pure mathematics, Demography, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1Per-year citation counts (last 5 years)
- References (count)
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29Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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