  Radio frequency (RF) wireless power transfer (WPT) is a promising technology
for sustainable support of massive Internet of Things (IoT). However, RF-WPT
systems are characterized by low efficiency due to channel attenuation, which
can be mitigated by precoders that adjust the transmission directivity. This
work considers a multi-antenna RF-WPT system with multiple non-linear energy
harvesting (EH) nodes with energy demands changing over discrete time slots.
This leads to the charging scheduling problem, which involves choosing the
precoders at each slot to minimize the total energy consumption and meet the EH
requirements. We model the problem as a Markov decision process and propose a
solution relying on a low-complexity beamforming and deep deterministic policy
gradient (DDPG). The results show that the proposed beamforming achieves
near-optimal performance with low computational complexity, and the DDPG-based
approach converges with the number of episodes and reduces the system's power
consumption, while the outage probability and the power consumption increase
with the number of devices.
