  This paper investigates the reinforcement learning for the relay selection in
the delay-constrained buffer-aided networks. The buffer-aided relay selection
significantly improves the outage performance but often at the price of higher
latency. On the other hand, modern communication systems such as the Internet
of Things often have strict requirement on the latency. It is thus necessary to
find relay selection policies to achieve good throughput performance in the
buffer-aided relay network while stratifying the delay constraint. With the
buffers employed at the relays and delay constraints imposed on the data
transmission, obtaining the best relay selection becomes a complicated
high-dimensional problem, making it hard for the reinforcement learning to
converge. In this paper, we propose the novel decision-assisted deep
reinforcement learning to improve the convergence. This is achieved by
exploring the a-priori information from the buffer-aided relay system. The
proposed approaches can achieve high throughput subject to delay constraints.
Extensive simulation results are provided to verify the proposed algorithms.
