A strategic learning algorithm for state-based games
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Publication:2173896
DOI10.1016/j.automatica.2019.108615zbMath1440.93017arXiv1809.05797OpenAlexW2995716012WikidataQ126542272 ScholiaQ126542272MaRDI QIDQ2173896
Yu Xing, Changxi Li, Fenghua He, Daizhan Cheng
Publication date: 17 April 2020
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1809.05797
Applications of game theory (91A80) Stochastic learning and adaptive control (93E35) Rationality and learning in game theory (91A26) Multi-agent systems (93A16)
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Cites Work
- Learning efficient Nash equilibria in distributed systems
- Stochastic uncoupled dynamics and Nash equilibrium
- How long to equilibrium? The communication complexity of uncoupled equilibrium procedures
- Learning by trial and error
- Three problems in learning mixed-strategy Nash equilibria
- Selecting efficient correlated equilibria through distributed learning
- State based potential games
- Distributed Optimization and Games: A Tutorial Overview
- Game-Theoretical Methods in Control of Engineering Systems: An Introduction to the Special Issue
- Achieving Pareto Optimality Through Distributed Learning
- Dynamic fictitious play, dynamic gradient play, and distributed convergence to Nash equilibria
- Stochastic Games
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