Value Iteration Networks
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.
NIPS