An Adaptive Network Slice Combination Algorithm Based on Multistep Temporal-Difference Learning

The existing multi-arm bandit (MAB) algorithm is proposed for network optimization, which does not consider the adjustment of learning step. This letter further targets the intelligent slice combination problem. Correspondingly, a multi-step temporal-difference learning (SC-MTD) algorithm is proposed for the adaptive combination, which consists of two-layer optimization. In the outer layer,
${Q}$
value is updated to attain lower network cost over time, which shows better tradeoff between high reliability and low latency. In the inner layer, elected degree is continuously modified, which indicates its changeability of learning step for the proposed algorithm. Finally, numerical results validate its convergence of the SC-MTD algorithm. Besides, lower network cost can be obtained, compared with the existing MAB algorithm.