This paper establishes the time consistent property, ie, the dynamic programming principle
(DPP), for learning mean field controls (MFCs). The key idea is to define the correct form of …

Dynamic programming principle (DPP) is fundamental for control and optimization, including
Markov decision problems (MDPs), reinforcement learning (RL), and more recently mean …

The dynamic programming principle (DPP) is fundamental for control and optimization,
including Markov decision problems (MDPs), reinforcement learning (RL), and, more …

Multi-agent reinforcement learning (MARL) has been applied to many challenging problems
including two-team computer games, autonomous drivings, and real-time biddings. Despite …

We study infinite horizon discounted mean field control (MFC) problems with common noise
through the lens of mean field Markov decision processes (MFMDP). We allow the agents to …

Abstract We present a Reinforcement Learning (RL) algorithm to solve infinite horizon
asymptotic Mean Field Game (MFG) and Mean Field Control (MFC) problems. Our approach …

We establish the convergence of the unified two-timescale Reinforcement Learning (RL)
algorithm presented by Angiuli et al. This algorithm provides solutions to Mean Field Game …

We study policy gradient for mean-field control in continuous time in a reinforcement
learning setting. By considering randomised policies with entropy regularisation, we derive a …

This paper studies the q-learning, recently coined as the continuous-time counterpart of Q-
learning by Jia and Zhou (2022c), for continuous time Mckean-Vlasov control problems in …

Mean Field Control (MFC) is a powerful approximation tool to solve large-scale Multi-Agent
Reinforcement Learning (MARL) problems. However, the success of MFC relies on the …