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.
For instance, Value Iteration Networks (VIN) REF approximates a planner by emulating value iteration using recurrent convolutional neural networks and maxpooling.
Value Iteration Networks
Advances in Neural Information Processing Systems 29 pages 2154--2162, 2016
Computer Science Mathematics