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Nash Equilibrium Seeking Over Undire...

Gadjov, Dian.

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Nash Equilibrium Seeking Over Undirected Graphs Via Multi-Agent Agreement.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nash Equilibrium Seeking Over Undirected Graphs Via Multi-Agent Agreement./
作者:	Gadjov, Dian.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	136 p.
附註:	Source: Masters Abstracts International, Volume: 57-01.
Contained By:	Masters Abstracts International57-01(E).
標題:	Systems science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10623975
ISBN:	9780355448955

Nash Equilibrium Seeking Over Undirected Graphs Via Multi-Agent Agreement.
Gadjov, Dian.

Nash Equilibrium Seeking Over Undirected Graphs Via Multi-Agent Agreement. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 136 p.

Source: Masters Abstracts International, Volume: 57-01.

Thesis (M.A.S.)--University of Toronto (Canada), 2017.

We consider the problem of distributed Nash equilibrium seeking over networks. In this setting, agents have limited information about the other players' actions and are forced to communicate with neighbouring agents. We start with a continuous-time gradient-play dynamics, with perfect information, under a strictly monotone pseudo-gradient assumption. In the partial information case we modify the gradient-play dynamics between players by expanding the action space. We propose an augmented gradient-play dynamics in which players can only communicate locally with their neighbours to compute an estimate of the other players' actions. We derive new dynamics based on the reformulation of the problem as a multi-agent coordination problem, over an undirected graph. We exploit the incremental passivity properties in the dynamics and show that a Laplacian feedback can be designed using relative estimates of their neighbours. We highlight that there is a trade-off between properties of the game and the communication graph.

ISBN: 9780355448955Subjects--Topical Terms:

3168411
Systems science.

Nash Equilibrium Seeking Over Undirected Graphs Via Multi-Agent Agreement.
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