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Intelligent reflecting surface (IRS) is a promising technology to assistdownlink information transmissions from a multi-antenna access point (AP) to areceiver. In this paper, we minimize the AP's transmit power by a jointoptimization of the AP's active beamforming and the IRS's passive beamforming.Due to uncertain channel conditions, we formulate a robust power minimizationproblem subject to the receiver's signal-to-noise ratio (SNR) requirement andthe 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 alower bound on the robust problem, which is integrated into the DRL frameworkand thus promoting a novel optimization-driven deep deterministic policygradient (DDPG) approach. In particular, when the DDPG algorithm generates apart of the action (e.g., passive beamforming), we can use the model-basedconvex approximation to optimize the other part (e.g., active beamforming) ofthe action more efficiently. Our simulation results demonstrate that theoptimization-driven DDPG algorithm can improve both the learning rate andreward performance significantly compared to the conventional model-free DDPGalgorithm.

Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications