{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/highway-value-iteration-networks","title":"Highway Value Iteration Networks","arxiv_id":"2406.03485","date":"2024-06-05","proceeding":null,"authors":["Yuhui Wang","Weida Li","Francesco Faccio","Qingyuan Wu","Jürgen Schmidhuber"],"abstract":"Value iteration networks (VINs) enable end-to-end learning for planning tasks by employing a differentiable \"planning module\" that approximates the value iteration algorithm. However, long-term planning remains a challenge because training very deep VINs is difficult. To address this problem, we embed highway value iteration -- a recent algorithm designed to facilitate long-term credit assignment -- into the structure of VINs. This improvement augments the \"planning module\" of the VIN with three additional components: 1) an \"aggregate gate,\" which constructs skip connections to improve information flow across many layers; 2) an \"exploration module,\" crafted to increase the diversity of information and gradient flow in spatial dimensions; 3) a \"filter gate\" designed to ensure safe exploration. The resulting novel highway VIN can be trained effectively with hundreds of layers using standard backpropagation. In long-term planning tasks requiring hundreds of planning steps, deep highway VINs outperform both traditional VINs and several advanced, very deep NNs.","url_abs":"https://arxiv.org/abs/2406.03485v1","url_pdf":"https://arxiv.org/pdf/2406.03485v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"safe-exploration","task_name":"Safe Exploration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2406.03485","atlas_url":"https://app.syntology.ai/?focus=2406.03485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03485"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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