Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

Tatarenko, Tatiana

Springer International Publishing AG

08/2018

171

Mole

Inglês

9783319880396

15 a 20 dias

454

Descrição não disponível.
Introduction and Research Motivation.- Backgrounds and Formulation of Contributions.- Logit Dynamics in Potential Games with Memoryless Players.- Stochastic Methods in Distributed Optimization and Game-Theoretic Learning.- Conclusion.- Appendix.
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distributed optimization;game-theoretic approach to optimization;learning algorithms;consensus-based algorithms;potential games;game theory;multi-agent optimization;game-theoretic learning;stochastic methods