Multi-agent reinforcement learning (RL) solves the problem of how each agent should
behave optimally in a stochastic environment in which multiple agents are learning …

Existing MARL algorithms have low efficiency in many-agent scenarios due to the complex
dynamic interaction when agents growing exponentially. Mean-field theory has been …

We investigate multi-agent imitation learning (IL) within the framework of mean field games
(MFGs), considering the presence of time-varying correlated signals. Existing MFG IL …

Mean field games (MFG) facilitate the otherwise intractable reinforcement learning (RL) in
large-scale multi-agent systems (MAS), through reducing interplays among agents to those …

Massively multiplayer online role-playing games often contain sophisticated in-game
economies. Many important real-world economic phenomena, such as inflation, economic …

In recent years, there has been an ever-increasing demand for building reliable and
versatile agents in applications arising from numerous fields including autonomous driving …

In today's rapidly evolving technological landscape, the development and advancement of
computational tools and algorithms have become paramount across a wide range of …

Imitation learning (IL) is a powerful approach for acquiring optimal policies from
demonstrated behaviors. However, applying IL to a large group of agents is arduous due to …

This study was conducted to examine the influence of 1-1-9 ambulance crews' empathy
ability and compassion fatigue on the quality of life in the COVID-19 situation, and to provide …