7 citations · 17 across the 8 of their papers we have counts for
8 papers
Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean Field Control Games
Andrea Angiuli, Jean-Pierre Fouque, Mathieu Laurière +1
Mean Field Control Games (MFCG), introduced in [Angiuli et al., 2022a], represent competitive games between a large number of large collaborative groups of agents in the infinite l…
Convergence of Multi-Scale Reinforcement Q-Learning Algorithms for Mean Field Game and Control Problems
Andrea Angiuli, Jean-Pierre Fouque, Mathieu Laurière +1
We establish the convergence of the unified two-timescale Reinforcement Learning (RL) algorithm presented in a previous work by Angiuli et al. This algorithm provides solutions to…
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces
Andrea Angiuli, Jean-Pierre Fouque, Ruimeng Hu +1
We present the development and analysis of a reinforcement learning (RL) algorithm designed to solve continuous-space mean field game (MFG) and mean field control (MFC) problems in…
Reinforcement Learning for Intra-and-Inter-Bank Borrowing and Lending Mean Field Control Game
Andrea Angiuli, Nils Detering, Jean-Pierre Fouque +2
We propose a mean field control game model for the intra-and-inter-bank borrowing and lending problem. This framework allows to study the competitive game arising between groups of…
Reinforcement Learning Algorithm for Mixed Mean Field Control Games
Andrea Angiuli, Nils Detering, Jean-Pierre Fouque +2
We present a new combined \textit{mean field control game} (MFCG) problem which can be interpreted as a competitive game between collaborating groups and its solution as a Nash equ…
Reinforcement Learning for Mean Field Games, with Applications to Economics
Andrea Angiuli, Jean-Pierre Fouque, Mathieu Lauriere
Mean field games (MFG) and mean field control problems (MFC) are frameworks to study Nash equilibria or social optima in games with a continuum of agents. These problems can be use…
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems
Andrea Angiuli, Jean-Pierre Fouque, Mathieu Laurière
We present a Reinforcement Learning (RL) algorithm to solve infinite horizon asymptotic Mean Field Game (MFG) and Mean Field Control (MFC) problems. Our approach can be described a…
Numerical Probabilistic Approach to MFG
Andrea Angiuli, Christy V. Graves, Houzhi Li +3
This project investigates numerical methods for solving fully coupled forward-backward stochastic differential equations (FBSDEs) of McKean-Vlasov type. Having numerical solvers fo…