Learning mean-field games with discounted and average costs

Name: Learning mean-field games with discounted and average costs
Author: Anahtarcı, Berkay, Karıksız, Can Deha, Saldı, N.

İsim	Learning mean-field games with discounted and average costs
Yazar	Anahtarcı, Berkay, Karıksız, Can Deha, Saldı, N.
Basım Tarihi:	2023
Basım Yeri	- Microtome Publishing
Konu	Mean-field games, Approximate Nash equilibrium, Fitted Q-iteration algo-rithm, Discounted-cost, Average-cost
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1532-4435
Kayıt Numarası	7f13317e-b850-4640-af22-2d16f8fdc921
Lokasyon	Natural and Mathematical Sciences
Tarih	2023
Notlar	TÜBİTAK
Örnek Metin	We consider learning approximate Nash equilibria for discrete-time mean-field games with stochastic nonlinear state dynamics subject to both average and discounted costs. To this end, we introduce a mean-field equilibrium (MFE) operator, whose fixed point is a mean-field equilibrium, i.e., equilibrium in the infinite population limit. We first prove that this operator is a contraction, and propose a learning algorithm to compute an approximate mean-field equilibrium by approximating the MFE operator with a random one. Moreover, using the contraction property of the MFE operator, we establish the error analysis of the proposed learning algorithm. We then show that the learned mean-field equilibrium constitutes an approximate Nash equilibrium for finite-agent games.
Cilt	24

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Learning mean-field games with discounted and average costs

Yazar Anahtarcı, Berkay, Karıksız, Can Deha, Saldı, N.

Basım Tarihi 2023

Basım Yeri - Microtome Publishing

Konu Mean-field games, Approximate Nash equilibrium, Fitted Q-iteration algo-rithm, Discounted-cost, Average-cost

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1532-4435

Kayıt Numarası 7f13317e-b850-4640-af22-2d16f8fdc921

Lokasyon Natural and Mathematical Sciences

Tarih 2023

Notlar TÜBİTAK

Örnek Metin We consider learning approximate Nash equilibria for discrete-time mean-field games with stochastic nonlinear state dynamics subject to both average and discounted costs. To this end, we introduce a mean-field equilibrium (MFE) operator, whose fixed point is a mean-field equilibrium, i.e., equilibrium in the infinite population limit. We first prove that this operator is a contraction, and propose a learning algorithm to compute an approximate mean-field equilibrium by approximating the MFE operator with a random one. Moreover, using the contraction property of the MFE operator, we establish the error analysis of the proposed learning algorithm. We then show that the learned mean-field equilibrium constitutes an approximate Nash equilibrium for finite-agent games.

Cilt 24