{"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-multimodal-transition-dynamics-for","title":"Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning","arxiv_id":"1705.00470","date":"2017-05-01","proceeding":null,"authors":["Thomas M. Moerland","Joost Broekens","Catholijn M. Jonker"],"abstract":"In this paper we study how to learn stochastic, multimodal transition\ndynamics in reinforcement learning (RL) tasks. We focus on evaluating\ntransition function estimation, while we defer planning over this model to\nfuture work. Stochasticity is a fundamental property of many task environments.\nHowever, discriminative function approximators have difficulty estimating\nmultimodal stochasticity. In contrast, deep generative models do capture\ncomplex high-dimensional outcome distributions. First we discuss why, amongst\nsuch models, conditional variational inference (VI) is theoretically most\nappealing for model-based RL. Subsequently, we compare different VI models on\ntheir ability to learn complex stochasticity on simulated functions, as well as\non a typical RL gridworld with multimodal dynamics. Results show VI\nsuccessfully predicts multimodal outcomes, but also robustly ignores these for\ndeterministic parts of the transition dynamics. In summary, we show a robust\nmethod to learn multimodal transitions using function approximation, which is a\nkey preliminary for model-based RL in stochastic domains.","url_abs":"http://arxiv.org/abs/1705.00470v2","url_pdf":"http://arxiv.org/pdf/1705.00470v2.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-multimodal-transition-dynamics-for","repo_url":"https://github.com/tmoer/multimodal_varinf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"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":"variational-inference","task_name":"Variational Inference"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1705.00470","atlas_url":"https://app.syntology.ai/?focus=1705.00470","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}