an opportunity to explore the representation hierarchy for optimal pedestrian scale, while the terminate trigger is to stop the sequence of coordinate transformation actions.

**Reward**: The reward function $r:=r_T$ at final time point $T$ serves the purpose of encouraging to learn localization policies that can detect pedestrians with high performance. Consequently a more strict detection criteria is enforced internally: The reward is set to $r = 1$ if the IoU between the finally predicted pedestrian bbox and its best-matched ground-truth bbox is above 0.7; It is set to $r=0$ if the IoU is below 0.2, for any IoU value in-between, the reward is set to be the IoU value.

Experiments
===========

Without loss of generality, our feature representation employed in this paper is based on a ResNet-50 network [@HeEtAl:cvpr16] pre-trained on Imagenet, where the convolutional layers and max pooling layers of the ResNet are engaged at multiple locations to produce multi-layer feature representations of the input image.

The initial bbox proposal network as illustrated in Fig. \[fig\_2\] is trained by stochastic gradient descent (SGD) with a learning rate of 0.001 and a momentum of 0.9 for 20k mini-batches, and with weight decay setting to 0.0005. As evidenced in e.g. [@RenEtAl:nips15; 