{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T21:32:26Z","timestamp":1770845546445,"version":"3.50.1"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,5]]},"DOI":"10.1109\/smc58881.2025.11343429","type":"proceedings-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:54:44Z","timestamp":1769633684000},"page":"1570-1575","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Modal World Models for Offline Visual Reinforcement Learning"],"prefix":"10.1109","author":[{"given":"Qi","family":"Wang","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University,MoE Key Lab of Artificial Intelligence, AI Institute,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Jin","sequence":"additional","affiliation":[{"name":"Eastern Institute of Technology,Ningbo Institute of Digital Twin,Ningbo,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baao","family":"Xie","sequence":"additional","affiliation":[{"name":"Eastern Institute of Technology,Ningbo Institute of Digital Twin,Ningbo,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaokang","family":"Yang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University,MoE Key Lab of Artificial Intelligence, AI Institute,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Zeng","sequence":"additional","affiliation":[{"name":"Eastern Institute of Technology,Ningbo Institute of Digital Twin,Ningbo,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"2052","article-title":"Off-policy deep reinforcement learning without exploration","author":"Fujimoto","year":"2019","journal-title":"ICML"},{"key":"ref2","first-page":"1179","article-title":"Conservative q-learning for offline reinforcement learning","volume":"33","author":"Kumar","year":"2020","journal-title":"NeurIPS"},{"key":"ref3","first-page":"28954","article-title":"Combo: Conservative offline model-based policy optimization","volume":"34","author":"Yu","year":"2021","journal-title":"NeurIPS"},{"key":"ref4","article-title":"Rambo-rl: Robust adversarial model-based offline reinforcement learning","author":"Rigter","year":"2022","journal-title":"NeurIPS"},{"key":"ref5","first-page":"36599","article-title":"When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning","volume":"35","author":"Niu","year":"2022","journal-title":"NeurIPS"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i20.35403"},{"key":"ref7","first-page":"1154","article-title":"Offline reinforcement learning from images with latent space models","volume-title":"Proceedings of Machine Learning Research","author":"Rafailov"},{"key":"ref8","article-title":"Challenges and opportunities in offline reinforcement learning from visual observations","author":"Lu","year":"2023","journal-title":"Transactions on Machine Learning Research"},{"key":"ref9","article-title":"Making offline rl online: Collaborative world models for offline visual reinforcement learning","author":"Wang","year":"2024","journal-title":"NeurIPS"},{"key":"ref10","article-title":"Mastering atari with discrete world models","author":"Hafner","year":"2021","journal-title":"ICLR"},{"key":"ref11","first-page":"8583","article-title":"Planning to explore via self-supervised world models","author":"Sekar","year":"2020","journal-title":"ICML"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl242"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20783"},{"key":"ref14","article-title":"Anti-exploration by random network distillation","author":"Nikulin","year":"2023","journal-title":"ICML"},{"key":"ref15","article-title":"Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning","author":"Yu","year":"2019","journal-title":"CoRL"},{"key":"ref16","article-title":"Mastering visual continuous control: Improved data-augmented reinforcement learning","author":"Yarats","year":"2021"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctt4cgngj.10"},{"key":"ref18","article-title":"Fast and accurate deep network learning by exponential linear units (elus)","author":"Clevert","year":"2015"},{"key":"ref19","first-page":"9870","article-title":"Decoupling representation learning from reinforcement learning","author":"Stooke","year":"2021","journal-title":"ICML"},{"key":"ref20","article-title":"Mastering visual continuous control: Improved data-augmented reinforcement learning","author":"Yarats","year":"2022","journal-title":"ICLR"},{"key":"ref21","article-title":"Vip: Towards universal visual reward and representation via value-implicit pre-training","author":"Ma","year":"2023","journal-title":"ICLR"},{"key":"ref22","article-title":"Generalizing consistency policy to visual rl with prioritized proximal experience regularization","author":"Li","year":"2024","journal-title":"NeurIPS"},{"key":"ref23","article-title":"Learning latent dynamics for planning from pixels","author":"Hafner","year":"2019","journal-title":"ICML"},{"key":"ref24","article-title":"Dream to control: Learning behaviors by latent imagination","author":"Hafner","year":"2020","journal-title":"ICLR"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-025-08744-2"},{"key":"ref26","article-title":"Storm: Efficient stochastic transformer based world models for reinforcement learning","author":"Zhang","year":"2023","journal-title":"NeurIPS"},{"key":"ref27","article-title":"Open-world reinforcement learning over long short-term imagination","author":"Li","year":"2025","journal-title":"ICLR"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-70368-3_2"},{"key":"ref29","article-title":"Exploration and anti-exploration with distributional random network distillation","author":"Yang","year":"2024","journal-title":"ICML"},{"key":"ref30","article-title":"Transfer rl across observation feature spaces via model-based regularization","author":"Sun","year":"2022","journal-title":"ICLR"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3292075"},{"key":"ref32","article-title":"Off-dynamics reinforcement learning: Training for transfer with domain classifiers","author":"Eysenbach","year":"2021","journal-title":"ICLR"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19809-0_7"},{"key":"ref34","article-title":"Disentangled world models: Learning to transfer semantic knowledge from distracting videos for reinforcement learning","author":"Wang","year":"2025"}],"event":{"name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","location":"Vienna, Austria","start":{"date-parts":[[2025,10,5]]},"end":{"date-parts":[[2025,10,8]]}},"container-title":["2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11342430\/11342431\/11343429.pdf?arnumber=11343429","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:51:02Z","timestamp":1770843062000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11343429\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,5]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/smc58881.2025.11343429","relation":{},"subject":[],"published":{"date-parts":[[2025,10,5]]}}}