  Energy harvesting technologies offer a promising solution to sustainably
power an ever-growing number of Internet of Things (IoT) devices. However, due
to the weak and transient natures of energy harvesting, IoT devices have to
work intermittently rendering conventional routing policies and energy
allocation strategies impractical. To this end, this paper, for the very first
time, developed a distributed multi-agent reinforcement algorithm known as
global actor-critic policy (GAP) to address the problem of routing policy and
energy allocation together for the energy harvesting powered IoT system. At the
training stage, each IoT device is treated as an agent and one universal model
is trained for all agents to save computing resources. At the inference stage,
packet delivery rate can be maximized. The experimental results show that the
proposed GAP algorithm achieves around 1.28 times and 1.24 times data
transmission rate than that of the Q-table and ESDSRAA algorithm, respectively.

Networking and Internet Architecture