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With the use of power domain non-orthogonal multiple access (NOMA) and backscatter communication (BAC), future sixth-generation ultra massive machine type communications networks are expected to connect large-scale Internet of things (IoT) devices. However, due to NOMA co-channel interference, the power allocation to large-scale IoT devices becomes critical. The existing convex optimization-based solutions are highly complex hence, it is difficult to find the optimal solution to the resource allocation problem in a highly dynamic environment. Therefore, this work develops an efficient model-free BACNOMA system to assist the base station for complex resource scheduling tasks in a dynamic BAC-NOMA IoT network. More specifically, we jointly optimize the transmit power of downlink IoT users and the reflection coefficient of uplink backscatter devices using a reinforcement learning algorithm, namely …