  This paper investigates reconfigurable intelligent surface (RIS)-assisted
full-duplex multiple-input single-output wireless system, where the beamforming
and RIS phase shifts are optimized to maximize the sum-rate for both single and
distributed RIS deployment schemes. The preference of using the single or
distributed RIS deployment scheme is investigated through three practical
scenarios based on the links' quality. The closed-form solution is derived to
optimize the beamforming vectors and a novel deep reinforcement learning (DRL)
algorithm is proposed to optimize the RIS phase shifts. Simulation results
illustrate that the choice of the deployment scheme depends on the scenario and
the links' quality. It is further shown that the proposed algorithm
significantly improves the sum-rate compared to the non-optimized scenario in
both single and distributed RIS deployment schemes. Besides, the proposed
beamforming derivation achieves a remarkable improvement compared to the
approximated derivation in previous works. Finally, the complexity analysis
confirms that the proposed DRL algorithm reduces the computation complexity
compared to the DRL algorithm in the literature.
