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

• Autore: Tatarenko, Tatiana
• Pubblicazione: Cham, : Springer, 2017
• Titolo uniforme: Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
• Descrizione fisica: ix, 171 p. : ill. ; 24 cm
• Soggetto topico: 60J20 - Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) [MSC 2020]; 91-XX - Game theory, economics, finance, and other social and behavioral sciences [MSC 2020]; 91A26 - Rationality and learning in game theory [MSC 2020]; 91A40 - Other game-theoretic models [MSC 2020]
• Soggetto non controllato: Consensus-based algorithms; Distributed Optimization; Game Theory; Game-theoretic approach to optimization; Game-theoretic learning; Learning algorithms; Multi-agent Optimization; Potential games; Stochastic methods
• Titolo autorizzato: Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: VAN00124064
• Lo trovi qui: Univ. Vanvitelli
• Localizzazioni e accesso elettronico: http://doi.org/10.1007/978-3-319-65479-9