  This paper investigates a wireless-powered hybrid reflecting intelligent
surface (hybrid RIS)-assisted multiple access system, where the RIS can harvest
energy from energy station (ES) transmitted radio frequency signal (RF), and
each reflecting element can flexibly switch between active mode, passive mode,
and idle mode. The objective is to minimize the maximum energy consumption of
the users by jointly optimizing the operating modes of each reflecting element,
the amplification factor of active elements, the transmit power, and
transmission time allocation, subject to quality-of-service (QoS) of each user
and the available energy constraint of RIS. In the formulated optimization
problem, the operating modes of each reflecting element are highly coupled with
the amplification coefficient of the active reflecting elements, making it a
challenging mixed-integer programming problem. To solve this problem, a
hierarchical optimization method based on deep reinforcement learning is
proposed, where the operating modes of each reflecting element and the
amplification coefficient of active elements are obtained by solving the outer
sub-problem using proximal policy optimization (PPO), and the transmit power
and transmission time allocation are obtained by solving the inner sub-problem
using convex optimization methods. Simulation results show that compared to the
baseline scheme, the proposed scheme can reduce user energy consumption by $70
\%$.
