Value Iteration Networks
Abstract
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.
Cite
Text
Tamar et al. "Value Iteration Networks." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/700Markdown
[Tamar et al. "Value Iteration Networks." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/tamar2017ijcai-value/) doi:10.24963/IJCAI.2017/700BibTeX
@inproceedings{tamar2017ijcai-value,
title = {{Value Iteration Networks}},
author = {Tamar, Aviv and Wu, Yi and Thomas, Garrett and Levine, Sergey and Abbeel, Pieter},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {4949-4953},
doi = {10.24963/IJCAI.2017/700},
url = {https://mlanthology.org/ijcai/2017/tamar2017ijcai-value/}
}