6 papers · 1 filter
Finite-player Optimal Stopping Games: Randomization, -potentiality, and Learning
Xin Guo, Mehdi Talbi, Qinxin Yan
Finite-player nonzero-sum optimal stopping games typically lead to coupled equilibrium systems whose complexity grows rapidly with the number of players. We introduce an independen…
Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data
Erhan Bayraktar, Martin Hernandez, Qinxin Yan +1
This paper develops a model-free framework for continuous-time mean-field control when the population evolves according to unknown controlled McKean--Vlasov dynamics and only discr…
Policy Gradient for Continuous-Time Mean-Field Control
Erhan Bayraktar, Martin Hernandez, Qinxin Yan +1
This paper develops a policy gradient method for entropy-regularized mean-field control in the discounted infinite-horizon setting. We consider randomized feedback policies and a c…
Implicit Regularization of Large Neural Networks via Mean-Field Formulation
Beatrice Acciaio, Jakob Heiss, Gudmund Pammer +1
We propose a mathematical framework to explain implicit regularization from early stopping during the training of overparametrized neural networks. In the mean-field limit, the par…
Iterative Schemes for Markov Perfect Equilibria
Felix Höfer, Mathieu Laurière, H. Mete Soner +1
We study Markov perfect equilibria in continuous-time dynamic games with finitely many symmetric players. The corresponding Nash system reduces to the Nash-Lasry-Lions equation for…
Learning algorithms for mean field optimal control
H. Mete Soner, Josef Teichmann, Qinxin Yan
We analyze an algorithm to numerically solve the mean-field optimal control problems by approximating the optimal feedback controls using neural networks with problem specific arch…