no code implementations • 9 Jul 2021 • René Carmona, Mathieu Laurière
Financial markets and more generally macro-economic models involve a large number of individuals interacting through variables such as prices resulting from the aggregate behavior of all the agents.
no code implementations • 2 Sep 2020 • René Carmona, Kenza Hamidouche, Mathieu Laurière, Zongjun Tan
In particular, the case in which the transition and utility functions depend on the state, the action of the controllers, and the mean of the state and the actions, is investigated.
no code implementations • 1 Sep 2020 • René Carmona, Kenza Hamidouche, Mathieu Laurière, Zongjun Tan
In particular, the case in which the transition and utility functions depend on the state, the action of the controllers, and the mean of the state and the actions, is investigated.
no code implementations • 28 Oct 2019 • René Carmona, Mathieu Laurière, Zongjun Tan
We study infinite horizon discounted Mean Field Control (MFC) problems with common noise through the lens of Mean Field Markov Decision Processes (MFMDP).
no code implementations • 9 Oct 2019 • René Carmona, Mathieu Laurière, Zongjun Tan
We investigate reinforcement learning for mean field control problems in discrete time, which can be viewed as Markov decision processes for a large number of exchangeable agents interacting in a mean field manner.
no code implementations • 5 Aug 2019 • René Carmona, Mathieu Laurière
The second method tackles a generic forward-backward stochastic differential equation system (FBSDE) of McKean-Vlasov type, and relies on suitable reformulation as a mean field control problem.
no code implementations • 13 Jul 2019 • René Carmona, Mathieu Laurière
Finally, we illustrate the fact that, although the first algorithm is specifically designed for mean field control problems, the second one is more general and can also be applied to the partial differential equation systems arising in the theory of mean field games.