  In the new generation of wireless communication systems, reconfigurable
intelligent surfaces (RIS) and simultaneously transmitting and reflecting
reconfigurable intelligent surfaces (STAR-RIS) have become competitive network
components to achieve intelligent and reconfigurable network environments.
However, existing work has not fully studied the deployment freedom of
STAR-RIS, which limits further improvements in network communication
performance. Therefore, this paper proposes a solution based on a deep
reinforcement learning algorithm to dynamically deploy STAR-RIS and hybrid
beamforming to improve the total communication rate of users in mobile wireless
networks. The paper constructs a STAR-RIS assisted multi-user multiple-input
single-output (MU-MISO) mobile wireless network and jointly optimizes the
dynamic deployment strategy of STAR-RIS and the hybrid beamforming strategy to
maximize the long-term total communication rate of users. To solve this
problem, the paper uses the Proximal Policy Optimization (PPO) algorithm to
optimize the deployment of STAR-RIS and the joint beamforming strategy of
STAR-RIS and the base station. The trained policy can maximize the downlink
transmission rate of the system and meet the real-time decision-making needs of
the system. Numerical simulation results show that compared with the
traditional scheme without using STAR-RIS and fixed STAR-RIS deployment, the
PPO method proposed in this paper can effectively improve the total
communication rate of wireless network users in the service area.
