Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Presents new, efficient methods for optimization in large-scale multi-agent systems Develops efficient optimization algorithms for three different information settings in multi-agent systems Sets optimization problems without common restrictive assumptions
| Autor: | Tatarenko, Tatiana |
|---|---|
| ISBN: | 9783319880396 |
| Sprache: | Englisch |
| Seitenzahl: | 171 |
| Produktart: | Kartoniert / Broschiert |
| Verlag: | Springer International Publishing |
| Veröffentlicht: | 15.08.2018 |
| Schlagworte: | consensus-based algorithms distributed optimization game-theoretic approach to optimization game-theoretic learning game theory learning algorithms multi-agent optimization potential games stochastic methods |
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