Summary
Multi-agent systems (MAS) are pervasive with applications in various areas such as computer networks, robotics, and power grids. For example, multi-robot systems play a critical role in our society, including industrial robots in car assembly lines, hundreds of drones in a light show, and many vehicles in future autonomous ride-sharing services. Sequential decision-making is crucial to construct functional, intelligent MAS that can meet our needs. Multi-agent reinforcement learning is an approach that facilitates machine learning through feedback that reinforces the desired behavior. However,
