Value Iteration Networks (VINs) are effective differentiable path planning
modules that can be used by agents to perform navigation while still
maintaining end-to-end differentiability of the entire architecture. Despite
their effectiveness, they suffer from several disadvantages including training
instability, random seed sensitivity, and other optimization problems. In this
work, we reframe VINs as recurrent-convolutional networks which demonstrates
that VINs couple recurrent convolutions with an unconventional max-pooling
activation. From this perspective, we argue that standard gated recurrent
update equations could potentially alleviate the optimization issues plaguing
VIN. The resulting architecture, which we call the Gated Path Planning Network,
is shown to empirically outperform VIN on a variety of metrics such as learning
speed, hyperparameter sensitivity, iteration count, and even generalization.
Furthermore, we show that this performance gap is consistent across different
maze transition types, maze sizes and even show success on a challenging 3D
environment, where the planner is only provided with first-person RGB images.

将值迭代网络（VIN）重新构建为递归卷积网络，提出了门控路径规划网络（Gated Path Planning Network）并提出标准门控循环更新方程可以缓解优化问题，取得了比 VIN 更好的性能，可用于多种场景下的导航规划。