{"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/learning-approximate-stochastic-transition","title":"Learning Approximate Stochastic Transition Models","arxiv_id":"1710.09718","date":"2017-10-26","proceeding":null,"authors":["Yuhang Song","Christopher Grimm","Xianming Wang","Michael L. Littman"],"abstract":"We examine the problem of learning mappings from state to state, suitable for\nuse in a model-based reinforcement-learning setting, that simultaneously\ngeneralize to novel states and can capture stochastic transitions. We show that\ncurrently popular generative adversarial networks struggle to learn these\nstochastic transition models but a modification to their loss functions results\nin a powerful learning algorithm for this class of problems.","url_abs":"http://arxiv.org/abs/1710.09718v1","url_pdf":"http://arxiv.org/pdf/1710.09718v1.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":"learning-approximate-stochastic-transition","repo_url":"https://github.com/YuhangSong/SGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"model-based-reinforcement-learning","task_name":"Model-based Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}