  Ultra low power devices make far-field wireless power transfer a viable
option for energy delivery despite the exponential attenuation. Electromagnetic
beams are constructed from the stations such that wireless energy is
directionally concentrated around the ultra low power devices. Energy
beamforming faces different challenges compare to information beamforming due
to the lack of feedback on channel state. Various methods have been proposed
such as one-bit channel feedback to enhance energy beamforming capacity, yet it
still has considerable computation overhead and need to be computed centrally.
Valuable resources and time is wasted on transfering control information back
and forth. In this paper, we propose a novel multiagent reinforcement
learning(MARL) formulation for codebook based beamforming control. It takes
advantage of the inherienntly distributed structure in a wirelessly powered
network and lay the ground work for fully locally computed beam control
algorithms. Source code can be found at
https://github.com/BaiLiping/WirelessPowerTransfer.
