  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.
