  Integrating artificial intelligence (AI) into wireless networks has drawn
significant interest in both industry and academia. A common solution is to
replace partial or even all modules in the conventional systems, which is often
lack of efficiency and robustness due to their ignoring of expert knowledge. In
this paper, we take deep reinforcement learning (DRL) based scheduling as an
example to investigate how expert knowledge can help with AI module in cellular
networks. A simulation platform, which has considered link adaption, feedback
and other practical mechanisms, is developed to facilitate the investigation.
Besides the traditional way, which is learning directly from the environment,
for training DRL agent, we propose two novel methods, i.e., learning from a
dual AI module and learning from the expert solution. The results show that,
for the considering scheduling problem, DRL training procedure can be improved
on both performance and convergence speed by involving the expert knowledge.
Hence, instead of replacing conventional scheduling module in the system,
adding a newly introduced AI module, which is capable to interact with the
conventional module and provide more flexibility, is a more feasible solution.
