  We introduce the value iteration network (VIN): a fully differentiable neural
network with a `planning module' embedded within. VINs can learn to plan, and
are suitable for predicting outcomes that involve planning-based reasoning,
such as policies for reinforcement learning. Key to our approach is a novel
differentiable approximation of the value-iteration algorithm, which can be
represented as a convolutional neural network, and trained end-to-end using
standard backpropagation. We evaluate VIN based policies on discrete and
continuous path-planning domains, and on a natural-language based search task.
We show that by learning an explicit planning computation, VIN policies
generalize better to new, unseen domains.
