  Multi-agent systems (MAS) constitute a significant role in exploring machine
intelligence and advanced applications. In order to deeply investigate
complicated interactions within MAS scenarios, we originally propose "GNN for
MBRL" model, which utilizes a state-spaced Graph Neural Networks with
Model-based Reinforcement Learning to address specific MAS missions (e.g.,
Billiard-Avoidance, Autonomous Driving Cars). In detail, we firstly used GNN
model to predict future states and trajectories of multiple agents, then
applied the Cross-Entropy Method (CEM) optimized Model Predictive Control to
assist the ego-agent planning actions and successfully accomplish certain MAS
tasks.
