  In this paper, we develop a knowledge-assisted deep reinforcement learning
(DRL) algorithm to design wireless schedulers in the fifth-generation (5G)
cellular networks with time-sensitive traffic. Since the scheduling policy is a
deterministic mapping from channel and queue states to scheduling actions, it
can be optimized by using deep deterministic policy gradient (DDPG). We show
that a straightforward implementation of DDPG converges slowly, has a poor
quality-of-service (QoS) performance, and cannot be implemented in real-world
5G systems, which are non-stationary in general. To address these issues, we
propose a theoretical DRL framework, where theoretical models from wireless
communications are used to formulate a Markov decision process in DRL. To
reduce the convergence time and improve the QoS of each user, we design a
knowledge-assisted DDPG (K-DDPG) that exploits expert knowledge of the
scheduler design problem, such as the knowledge of the QoS, the target
scheduling policy, and the importance of each training sample, determined by
the approximation error of the value function and the number of packet losses.
Furthermore, we develop an architecture for online training and inference,
where K-DDPG initializes the scheduler off-line and then fine-tunes the
scheduler online to handle the mismatch between off-line simulations and
non-stationary real-world systems. Simulation results show that our approach
reduces the convergence time of DDPG significantly and achieves better QoS than
existing schedulers (reducing 30% ~ 50% packet losses). Experimental results
show that with off-line initialization, our approach achieves better initial
QoS than random initialization and the online fine-tuning converges in few
minutes.
