Existing multi-agent reinforcement learning methods are limited typically to a small number
of agents. When the agent number increases largely, the learning becomes intractable due …

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 …

Recently, despite massively multiplayer online role-playing game (MMORPG) based on the
PC implementation environment in mobile games, related fraudulent and illegal activities …

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 …

As the concept of the Metaverse rapidly spread, massively multiplayer online role-playing
game (MMORPG), one of the Metaverse games, received public's attention again …

The recent mean field game (MFG) formalism has enabled the application of inverse
reinforcement learning (IRL) methods in large-scale multi-agent systems, with the goal of …

As the online service industry has continued to grow, illegal activities in the online world
have drastically increased and become more diverse. Most illegal activities occur …

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 …

In the context of the new economic development in the post-pandemic era," play" labor as an
important component of digital work has become an inexhaustible driving force for the …

In this article, we investigate the Nash-seeking problem of a set of agents, playing an infinite
network aggregative Markov game. In particular, we focus on a noncooperative framework …