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- W3201565443 abstract "We apply the generalized conditional gradient algorithm to potential mean field games and we show its well-posedeness. It turns out that this method can be interpreted as a learning method called fictitious play. More precisely, each step of the generalized conditional gradient method amounts to compute the best-response of the representative agent, for a predicted value of the coupling terms of the game. We show that for the learning sequence δk = 2/(k + 2), the potential cost converges in O(1/k), the exploitability and the variables of the problem (distribution, congestion, price, value function and control terms) converge in O(1/ √ k), for specific norms." @default.
- W3201565443 created "2021-09-27" @default.
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- W3201565443 date "2022-10-07" @default.
- W3201565443 modified "2023-09-26" @default.
- W3201565443 title "Generalized conditional gradient and learning in potential mean field games" @default.
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