{"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/propagation-networks-for-model-based-control","title":"Propagation Networks for Model-Based Control Under Partial Observation","arxiv_id":"1809.11169","date":"2018-09-28","proceeding":null,"authors":["Yunzhu Li","Jiajun Wu","Jun-Yan Zhu","Joshua B. Tenenbaum","Antonio Torralba","Russ Tedrake"],"abstract":"There has been an increasing interest in learning dynamics simulators for\nmodel-based control. Compared with off-the-shelf physics engines, a learnable\nsimulator can quickly adapt to unseen objects, scenes, and tasks. However,\nexisting models like interaction networks only work for fully observable\nsystems; they also only consider pairwise interactions within a single time\nstep, both restricting their use in practical systems. We introduce Propagation\nNetworks (PropNet), a differentiable, learnable dynamics model that handles\npartially observable scenarios and enables instantaneous propagation of signals\nbeyond pairwise interactions. Experiments show that our propagation networks\nnot only outperform current learnable physics engines in forward simulation,\nbut also achieve superior performance on various control tasks. Compared with\nexisting model-free deep reinforcement learning algorithms, model-based control\nwith propagation networks is more accurate, efficient, and generalizable to\nnew, partially observable scenes and tasks.","url_abs":"http://arxiv.org/abs/1809.11169v2","url_pdf":"http://arxiv.org/pdf/1809.11169v2.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":[{"paper_slug":"propagation-networks-for-model-based-control","repo_url":"https://github.com/YunzhuLi/PropNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.11169","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}