  Distributed access control is a crucial component for massive machine type
communication (mMTC). In this communication scenario, centralized resource
allocation is not scalable because resource configurations have to be sent
frequently from the base station to a massive number of devices. We investigate
distributed reinforcement learning for resource selection without relying on
centralized control. Another important feature of mMTC is the sporadic and
dynamic change of traffic. Existing studies on distributed access control
assume that traffic load is static or they are able to gradually adapt to the
dynamic traffic. We minimize the adaptation period by training TinyQMIX, which
is a lightweight multi-agent deep reinforcement learning model, to learn a
distributed wireless resource selection policy under various traffic patterns
before deployment. Therefore, the trained agents are able to quickly adapt to
dynamic traffic and provide low access delay. Numerical results are presented
to support our claims.
