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Game-theoretic learning and distributed optimization in memoryless multi-agent systems
This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained...
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| Lenguaje: | eng |
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Springer
2017
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| Acceso en línea: | https://dx.doi.org/10.1007/978-3-319-65479-9 http://cds.cern.ch/record/2287908 |