  The high demand for data rate in the next generation of wireless
communication could be ensured by Non-Orthogonal Multiple Access (NOMA)
approach in the millimetre-wave (mmW) frequency band. Joint power allocation
and beamforming of mmW-NOMA systems is mandatory which could be met by
optimization approaches. To this end, we have exploited Deep Reinforcement
Learning (DRL) approach due to policy generation leading to an optimized
sum-rate of users. Actor-critic phenomena are utilized to measure the immediate
reward and provide the new action to maximize the overall Q-value of the
network. The immediate reward has been defined based on the summation of the
rate of two users regarding the minimum guaranteed rate for each user and the
sum of consumed power as the constraints. The simulation results represent the
superiority of the proposed approach rather than the Time-Division Multiple
Access (TDMA) and another NOMA optimized strategy in terms of sum-rate of
users.
