  We consider online learning for optimal network slice placement under the
assumption that slice requests arrive according to a non-stationary Poisson
process. We propose a framework based on Deep Reinforcement Learning (DRL)
combined with a heuristic to design algorithms. We specifically design two
pure-DRL algorithms and two families of hybrid DRL-heuristic algorithms. To
validate their performance, we perform extensive simulations in the context of
a large-scale operator infrastructure. The evaluation results show that the
proposed hybrid DRL-heuristic algorithms require three orders of magnitude of
learning episodes less than pure-DRL to achieve convergence. This result
indicates that the proposed hybrid DRL-heuristic approach is more reliable than
pure-DRL in a real non-stationary network scenario.
