4 papers
Residual-Controlled Multiplier Learning for Stochastic Constrained Decision-Making
Kang Liu, Jianchen Hu, Ziyu Qu +3
Stochastic constrained decision-making requires optimizing performance objectives while enforcing statistical requirements such as safety or fairness. However, standard primal--dua…
Convergence of Actor-Critic Learning for Mean Field Games and Mean Field Control in Continuous Spaces
Jean-Pierre Fouque, Mathieu Laurière, Mengrui Zhang
We establish the convergence of the deep actor-critic reinforcement learning algorithm presented in [Angiuli et al., 2023a] in the setting of continuous state and action spaces wit…
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…