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Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging and the effectiveness of such a solution is limited due to the absence of closed-loop power control. In this paper, we design a prototype of layer-based transmit power pool by utilizing multi-agent reinforcement learning to provide open-loop power control and offload the computing tasks at the base station (BS) side. IoT users in each layer decide their own transmit power level from this layer-based power pool, instead of transmitting on the allocated sub-channel with allocated transmit power level. The proposed algorithm does not require any information exchange between IoT users and does not rely on any assistance from the BS. Numerical results confirm that the double deep Q network …