  A novel simulator called VMAgent is introduced to help RL researchers better
explore new methods, especially for virtual machine scheduling. VMAgent is
inspired by practical virtual machine (VM) scheduling tasks and provides an
efficient simulation platform that can reflect the real situations of cloud
computing. Three scenarios (fading, recovering, and expansion) are concluded
from practical cloud computing and corresponds to many reinforcement learning
challenges (high dimensional state and action spaces, high non-stationarity,
and life-long demand). VMAgent provides flexible configurations for RL
researchers to design their customized scheduling environments considering
different problem features. From the VM scheduling perspective, VMAgent also
helps to explore better learning-based scheduling solutions.
