  The evaluation of the impact of using Machine Learning in the management of
softwarized networks is considered in multiple research works. Beyond that, we
propose to evaluate the robustness of online learning for optimal network slice
placement. A major assumption to this study is to consider that slice request
arrivals are non-stationary. In this context, we simulate unpredictable network
load variations and compare two Deep Reinforcement Learning (DRL) algorithms: a
pure DRL-based algorithm and a heuristically controlled DRL as a hybrid
DRL-heuristic algorithm, to assess the impact of these unpredictable changes of
traffic load on the algorithms performance. We conduct extensive simulations of
a large-scale operator infrastructure. The evaluation results show that the
proposed hybrid DRL-heuristic approach is more robust and reliable in case of
unpredictable network load changes than pure DRL as it reduces the performance
degradation. These results are follow-ups for a series of recent research we
have performed showing that the proposed hybrid DRL-heuristic approach is
efficient and more adapted to real network scenarios than pure DRL.
