{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T04:51:14Z","timestamp":1774587074815,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>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.This paper is a significantly abridged and IJCAI audience targeted version of the original NIPS 2016 paper with the same title, available here: https:\/\/arxiv.org\/abs\/1602.02867<\/jats:p>","DOI":"10.24963\/ijcai.2017\/700","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"4949-4953","source":"Crossref","is-referenced-by-count":124,"title":["Value Iteration Networks"],"prefix":"10.24963","author":[{"given":"Aviv","family":"Tamar","sequence":"first","affiliation":[{"name":"UC Berkeley"}]},{"given":"Yi","family":"Wu","sequence":"additional","affiliation":[{"name":"UC Berkeley"}]},{"given":"Garrett","family":"Thomas","sequence":"additional","affiliation":[{"name":"UC Berkeley"}]},{"given":"Sergey","family":"Levine","sequence":"additional","affiliation":[{"name":"UC Berkeley"}]},{"given":"Pieter","family":"Abbeel","sequence":"additional","affiliation":[{"name":"UC Berkeley"},{"name":"OpenAI"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:55:11Z","timestamp":1501242911000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/700"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/700","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}