  Using RF signals for wireless sensing has gained increasing attention.
However, due to the unwanted multi-path fading in uncontrollable radio
environments, the accuracy of RF sensing is limited. Instead of passively
adapting to the environment, in this paper, we consider the scenario where an
intelligent metasurface is deployed for sensing the existence and locations of
3D objects. By programming its beamformer patterns, the metasurface can provide
desirable propagation properties. However, achieving a high sensing accuracy is
challenging, since it requires the joint optimization of the beamformer
patterns and mapping of the received signals to the sensed outcome. To tackle
this challenge, we formulate an optimization problem for minimizing the
cross-entropy loss of the sensing outcome, and propose a deep reinforcement
learning algorithm to jointly compute the optimal beamformer patterns and the
mapping of the received signals. Simulation results verify the effectiveness of
the proposed algorithm and show how the sizes of the metasurface and the target
space influence the sensing accuracy.
