Search Results for author: René Carmona

Found 7 papers, 0 papers with code

Deep Learning for Mean Field Games and Mean Field Control with Applications to Finance

no code implementations9 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.

Policy Optimization for Linear-Quadratic Zero-Sum Mean-Field Type Games

no code implementations2 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.

Vocal Bursts Type Prediction

Linear-Quadratic Zero-Sum Mean-Field Type Games: Optimality Conditions and Policy Optimization

no code implementations1 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.

Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning

no code implementations28 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).

General Reinforcement Learning Q-Learning +1

Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods

no code implementations9 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.

Policy Gradient Methods reinforcement-learning +1

Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: II -- The Finite Horizon Case

no code implementations5 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.

Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: I -- The Ergodic Case

no code implementations13 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.

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