Decentralized Learning for Optimality in Stochastic Dynamic Teams and Games With Local Control and Global State Information Article Swipe
Bora Yongacoglu
,
Gürdal Arslan
,
Serdar Yüksel
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tac.2021.3121228
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tac.2021.3121228
Stochastic dynamic teams and games are rich models for decentralized systems and challenging testing grounds for multi-agent learning. Previous work that guaranteed team optimality assumed stateless dynamics, or an explicit coordination mechanism, or joint-control sharing. In this paper, we present an algorithm with guarantees of convergence to team optimal policies in teams and common interest games. The algorithm is a two-timescale method that uses a variant of Q-learning on the finer timescale to perform policy evaluation while exploring the policy space on the coarser timescale. Agents following this algorithm are "independent learners": they use only local controls, local cost realizations, and global state information, without access to controls of other agents. The results presented here are the first, to our knowledge, to give formal guarantees of convergence to team optimality using independent learners in stochastic dynamic teams and common interest games.
Related Topics
Concepts
Computer science
Convergence (economics)
Stateless protocol
Control (management)
Mathematical optimization
State space
State (computer science)
Stochastic control
Sequential game
Optimal control
Artificial intelligence
Game theory
Mathematical economics
Mathematics
Economics
Algorithm
Economic growth
Statistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/tac.2021.3121228
- OA Status
- green
- Cited By
- 8
- References
- 81
- Related Works
- 20
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W3111119344Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/tac.2021.3121228Digital Object Identifier
- Title
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Decentralized Learning for Optimality in Stochastic Dynamic Teams and Games With Local Control and Global State InformationWork title
- Type
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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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2021-10-26Full publication date if available
- Authors
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Bora Yongacoglu, Gürdal Arslan, Serdar YükselList of authors in order
- Landing page
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https://doi.org/10.1109/tac.2021.3121228Publisher landing page
- 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/1903.05812Direct OA link when available
- Concepts
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Computer science, Convergence (economics), Stateless protocol, Control (management), Mathematical optimization, State space, State (computer science), Stochastic control, Sequential game, Optimal control, Artificial intelligence, Game theory, Mathematical economics, Mathematics, Economics, Algorithm, Economic growth, StatisticsTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 1, 2023: 1, 2022: 2, 2021: 2, 2020: 2Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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