  Dynamic routing in software-defined networking (SDN) can be viewed as a
centralized decision-making problem. Most of the existing deep reinforcement
learning (DRL) agents can address it, thanks to the deep neural network
(DNN)incorporated. However, fully-connected feed-forward neural network (FFNN)
is usually adopted, where spatial correlation and temporal variation of traffic
flows are ignored. This drawback usually leads to significantly high
computational complexity due to large number of training parameters. To
overcome this problem, we propose a novel model-free framework for dynamic
routing in SDN, which is referred to as spatio-temporal deterministic policy
gradient (STDPG) agent. Both the actor and critic networks are based on
identical DNN structure, where a combination of convolutional neural network
(CNN) and long short-term memory network (LSTM) with temporal attention
mechanism, CNN-LSTM-TAM, is devised. By efficiently exploiting spatial and
temporal features, CNNLSTM-TAM helps the STDPG agent learn better from the
experience transitions. Furthermore, we employ the prioritized experience
replay (PER) method to accelerate the convergence of model training. The
experimental results show that STDPG can automatically adapt for current
network environment and achieve robust convergence. Compared with a number
state-ofthe-art DRL agents, STDPG achieves better routing solutions in terms of
the average end-to-end delay.
