{"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/temporal-regularization-in-markov-decision","title":"Temporal Regularization in Markov Decision Process","arxiv_id":"1811.00429","date":"2018-11-01","proceeding":null,"authors":["Pierre Thodoroff","Audrey Durand","Joelle Pineau","Doina Precup"],"abstract":"Several applications of Reinforcement Learning suffer from instability due to\nhigh variance. This is especially prevalent in high dimensional domains.\nRegularization is a commonly used technique in machine learning to reduce\nvariance, at the cost of introducing some bias. Most existing regularization\ntechniques focus on spatial (perceptual) regularization. Yet in reinforcement\nlearning, due to the nature of the Bellman equation, there is an opportunity to\nalso exploit temporal regularization based on smoothness in value estimates\nover trajectories. This paper explores a class of methods for temporal\nregularization. We formally characterize the bias induced by this technique\nusing Markov chain concepts. We illustrate the various characteristics of\ntemporal regularization via a sequence of simple discrete and continuous MDPs,\nand show that the technique provides improvement even in high-dimensional Atari\ngames.","url_abs":"http://arxiv.org/abs/1811.00429v2","url_pdf":"http://arxiv.org/pdf/1811.00429v2.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":"temporal-regularization-in-markov-decision","repo_url":"https://github.com/pierthodo/temporal_regularization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"temporal-regularization-in-markov-decision","repo_url":"https://github.com/sauxpa/ML_101","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"atari-games","task_name":"Atari Games"},{"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}