  Intelligent reflecting surface (IRS) is a promising technology to assist
downlink information transmissions from a multi-antenna access point (AP) to a
receiver. In this paper, we minimize the AP's transmit power by a joint
optimization of the AP's active beamforming and the IRS's passive beamforming.
Due to uncertain channel conditions, we formulate a robust power minimization
problem subject to the receiver's signal-to-noise ratio (SNR) requirement and
the IRS's power budget constraint. We propose a deep reinforcement learning
(DRL) approach that can adapt the beamforming strategies from past experiences.
To improve the learning performance, we derive a convex approximation as a
lower bound on the robust problem, which is integrated into the DRL framework
and thus promoting a novel optimization-driven deep deterministic policy
gradient (DDPG) approach. In particular, when the DDPG algorithm generates a
part of the action (e.g., passive beamforming), we can use the model-based
convex approximation to optimize the other part (e.g., active beamforming) of
the action more efficiently. Our simulation results demonstrate that the
optimization-driven DDPG algorithm can improve both the learning rate and
reward performance significantly compared to the conventional model-free DDPG
algorithm.
