  In this paper, the downlink packet scheduling problem for cellular networks
is modeled, which jointly optimizes throughput, fairness and packet drop rate.
Two genie-aided heuristic search methods are employed to explore the solution
space. A deep reinforcement learning (DRL) framework with A2C algorithm is
proposed for the optimization problem. Several methods have been utilized in
the framework to improve the sampling and training efficiency and to adapt the
algorithm to a specific scheduling problem. Numerical results show that DRL
outperforms the baseline algorithm and achieves similar performance as
genie-aided methods without using the future information.
