  In this work, we examine an intelligent reflecting surface (IRS) assisted
downlink non-orthogonal multiple access (NOMA) scenario with the aim of
maximizing the sum rate of users. The optimization problem at the IRS is quite
complicated, and non-convex, since it requires the tuning of the phase shift
reflection matrix. Driven by the rising deployment of deep reinforcement
learning (DRL) techniques that are capable of coping with solving non-convex
optimization problems, we employ DRL to predict and optimally tune the IRS
phase shift matrices. Simulation results reveal that IRS assisted NOMA based on
our utilized DRL scheme achieves high sum rate compared to OMA based one, and
as the transmit power increases, the capability of serving more users
increases. Furthermore, results show that imperfect successive interference
cancellation (SIC) has a deleterious impact on the data rate of users
performing SIC. As the imperfection increases by ten times, the rate decreases
by more than 10%.
