  The Internet of Things (IoT) extends the Internet connectivity into billions
of IoT devices around the world, where the IoT devices collect and share
information to reflect status of the physical world. The Autonomous Control
System (ACS), on the other hand, performs control functions on the physical
systems without external intervention over an extended period of time. The
integration of IoT and ACS results in a new concept - autonomous IoT (AIoT).
The sensors collect information on the system status, based on which the
intelligent agents in the IoT devices as well as the Edge/Fog/Cloud servers
make control decisions for the actuators to react. In order to achieve
autonomy, a promising method is for the intelligent agents to leverage the
techniques in the field of artificial intelligence, especially reinforcement
learning (RL) and deep reinforcement learning (DRL) for decision making. In
this paper, we first provide a tutorial of DRL, and then propose a general
model for the applications of RL/DRL in AIoT. Next, a comprehensive survey of
the state-of-art research on DRL for AIoT is presented, where the existing
works are classified and summarized under the umbrella of the proposed general
DRL model. Finally, the challenges and open issues for future research are
identified.
