Heterogeneous multi-agent cooperative decisionmaking is one of the kernel problems in
collective intelligence field. Reinforcement learning may be an effective technology to …

In this paper, we consider a multi-agent system with N cooperative agents where each agent
privately observes its own private type and publicly observes each others' actions. We …

Multi-agent reinforcement learning is currently mainly used in many real-time strategy
games. For example, StarCraft, UAV combat. Multi-agent reinforcement learning algorithms …

There are many multi-agent systems in life, such as driving vehicles, playing football games,
and even bees building their hives. These systems are cooperative or competitive among …

In cooperative multi-agent reinforcement learning, a team of agents works together to
achieve a common goal. Different environments or tasks may require varying degrees of …

In the domain of multi-agent reinforcement learning (MARL), the limited information
availability, complex agent interactions, and individual capabilities among agents often pose …

Multi-agent reinforcement learning is promising for real-world applications as it encourages
agents to perceive and interact with their surrounding environment autonomously. However …

Despite the recent advancement in multi-agent reinforcement learning (MARL), the MARL
agents easily overfit the training environment and perform poorly in evaluation scenarios …

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

Training for multi-agent reinforcement learning (MARL) is a time-consuming process caused
by distribution shift of each agent. One drawback is that strategy of each agent in MARL is …