  We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input
single-output (MISO) system for downlink transmission. We compare the
performance of Deep Reinforcement Learning (DRL) and conventional optimization
methods in finding optimal phase shifts of the IRS elements to maximize the
user signal-to-noise (SNR) ratio. Furthermore, we evaluate the robustness of
these methods to channel impairments and changes in the system. We demonstrate
numerically that DRL solutions show more robustness to noisy channels and user
mobility.
