  Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as
one of the key enabling technologies for the future six generation (6G)
wireless communication systems. 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. 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.
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-bard beamforming problem, especially when the signals at
RIS-empowered THz communication networks experience multiple hops.
