In the future, with the advent of the Internet of ings (IoT), wireless sensors, and multiple 5G applications yet to be developed, an indoor room might be lled with 1000s of devices. ese devices will have dierent ality of Service (QoS) demands and resource constraints, such as mobility, hardware, and eciency requirements. e THz band has a massive greeneld spectrum and is envisioned to cater to these dense-indoor deployments. However, THz has multiple caveats, such as high absorption rate, limited coverage range, low transmit power, sensitivity to mobility, and frequent outages, making it challenging to deploy. THz might compel networks to be dependent on additional infrastructure, which might not be protable for network operators and can even result in inecient resource utilization for devices demanding low to moderate data rates. Using distributed Device-to-Device (D2D) communication in the THz, we can cater to these ultra-dense low data rate type applications in a constrained resource situation. We propose a 2-Layered distributed D2D model, where devices use coordinated multi-agent reinforcement learning (MARL) to maximize eciency and user coverage for dense-indoor deployment. We explore the choice of features required to train the algorithms and how it impacts the system eciency. We show that densication and mobility in a network can be used to further the limited coverage range of THz devices, without the need for extra infrastructure or resources.