  With the accelerated development of immersive applications and the explosive
increment of internet-of-things (IoT) terminals, 6G would introduce terahertz
(THz) massive multiple-input multiple-output non-orthogonal multiple access
(MIMO-NOMA) technologies to meet the ultra-high-speed transmission and massive
connectivity requirements. Nevertheless, the unreliability of THz transmissions
and the extreme heterogeneity of device requirements pose critical challenges
for practical applications. To address these challenges, we propose a novel
smart reconfigurable THz MIMO-NOMA framework, which can realize customizable
and intelligent communications by flexibly and coordinately reconfiguring
hybrid beams through the cooperation between access points (APs) and
reconfigurable intelligent surfaces (RISs). The optimization problem is
formulated as a decentralized partially-observable Markov decision process
(Dec-POMDP) to maximize the network energy efficiency, while guaranteeing the
diversified users' performance, via a joint RIS element selection, coordinated
discrete phase-shift control, and power allocation strategy. To solve the above
non-convex, strongly coupled, and highly complex mixed integer nonlinear
programming (MINLP) problem, we propose a novel multi-agent deep reinforcement
learning (MADRL) algorithm, namely graph-embedded value-decomposition
actor-critic (GE-VDAC), that embeds the interaction information of agents, and
learns a locally optimal solution through a distributed policy. Numerical
results demonstrate that the proposed algorithm achieves highly customized
communications and outperforms traditional MADRL algorithms.
