  Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as
one of the key enabling technologies for the future sixth generation (6G)
wireless communication systems scaled up beyond massive multiple input multiple
output (Massive-MIMO) technology. However, very high propagation attenuations
and molecular absorptions of THz frequencies often limit the signal
transmission distance and coverage range. Benefited from the recent
breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing
smart radio propagation environment, we propose a novel hybrid beamforming
scheme for the multi-hop RIS-assisted communication networks to improve the
coverage range at THz-band frequencies. Particularly, multiple passive and
controllable RISs are deployed to assist the transmissions between the base
station (BS) and multiple single-antenna users. We investigate the joint design
of digital beamforming matrix at the BS and analog beamforming matrices at the
RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to
combat the propagation loss. To improve the convergence of the proposed
DRL-based algorithm, two algorithms are then designed to initialize the digital
beamforming and the analog beamforming matrices utilizing the alternating
optimization technique. Simulation results show that our proposed scheme is
able to improve 50\% more coverage range of THz communications compared with
the benchmarks. Furthermore, it is also shown that our proposed DRL-based
method is a state-of-the-art method to solve the NP-hard beamforming problem,
especially when the signals at RIS-assisted THz communication networks
experience multiple hops.
