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This paper investigates the reinforcement learning for the relay selection inthe delay-constrained buffer-aided networks. The buffer-aided relay selectionsignificantly improves the outage performance but often at the price of higherlatency. On the other hand, modern communication systems such as the Internetof Things often have strict requirement on the latency. It is thus necessary tofind relay selection policies to achieve good throughput performance in thebuffer-aided relay network while stratifying the delay constraint. With thebuffers employed at the relays and delay constraints imposed on the datatransmission, obtaining the best relay selection becomes a complicatedhigh-dimensional problem, making it hard for the reinforcement learning toconverge. In this paper, we propose the novel decision-assisted deepreinforcement learning to improve the convergence. This is achieved byexploring the a-priori information from the buffer-aided relay system. Theproposed approaches can achieve high throughput subject to delay constraints.Extensive simulation results are provided to verify the proposed algorithms.

Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning