Significant advances have recently been achieved in Multi-Agent Reinforcement Learning
(MARL) which tackles sequential decision-making problems involving multiple participants …

Non-cooperative and cooperative games with a very large number of players have many
applications but remain generally intractable when the number of players increases …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

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 …

Reinforcement learning algorithms typically consider discrete-time dynamics, even though
the underlying systems are often continuous in time. In this paper, we introduce a model …

Reinforcement learning algorithms commonly seek to optimize policies for solving one
particular task. How should we explore an unknown dynamical system such that the …

Recent advances at the intersection of dense large graph limits and mean field games have
begun to enable the scalable analysis of a broad class of dynamical sequential games with …

Mean field control (MFC) is an effective way to mitigate the curse of dimensionality of
cooperative multi-agent reinforcement learning (MARL) problems. This work considers a …

This paper presents a general mean-field game (GMFG) framework for simultaneous
learning and decision making in stochastic games with a large population. It first establishes …

The marriage between mean-field theory and reinforcement learning has shown a great
capacity to solve large-scale control problems with homogeneous agents. To break the …