The paper presents a joint beamforming algorithm using statistical channel
state information (S-CSI) for reconfigurable intelligent surfaces (RIS) for
multiuser MISO wireless communications. We used S-CSI, which is a long-term
average of the cascaded channel as opposed to instantaneous CSI utilized in
most existing works. Through this method, the overhead of channel estimation is
dramatically reduced. We propose a proximal policy optimization (PPO) algorithm
which is a well-known actor-critic based reinforcement learning (RL) algorithm
to solve the optimization problem. To test the efficacy of this algorithm,
simulation results are presented along with evaluations of key system
parameters, including the Rician factor and RIS location, on the achievable sum
rate of the users