  In this paper, we propose a deep state-action-reward-state-action (SARSA)
$\lambda$ learning approach for optimising the uplink resource allocation in
non-orthogonal multiple access (NOMA) aided ultra-reliable low-latency
communication (URLLC). To reduce the mean decoding error probability in
time-varying network environments, this work designs a reliable learning
algorithm for providing a long-term resource allocation, where the reward
feedback is based on the instantaneous network performance. With the aid of the
proposed algorithm, this paper addresses three main challenges of the reliable
resource sharing in NOMA-URLLC networks: 1) user clustering; 2) Instantaneous
feedback system; and 3) Optimal resource allocation. All of these designs
interact with the considered communication environment. Lastly, we compare the
performance of the proposed algorithm with conventional Q-learning and SARSA
Q-learning algorithms. The simulation outcomes show that: 1) Compared with the
traditional Q learning algorithms, the proposed solution is able to converges
within \myb{200} episodes for providing as low as $10^{-2}$ long-term mean
error; 2) NOMA assisted URLLC outperforms traditional OMA systems in terms of
decoding error probabilities; and 3) The proposed feedback system is efficient
for the long-term learning process.
