{"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/data-efficient-reinforcement-learning-with","title":"Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control","arxiv_id":"1706.06491","date":"2017-06-20","proceeding":null,"authors":["Sanket Kamthe","Marc Peter Deisenroth"],"abstract":"Trial-and-error based reinforcement learning (RL) has seen rapid advancements\nin recent times, especially with the advent of deep neural networks. However,\nthe majority of autonomous RL algorithms require a large number of interactions\nwith the environment. A large number of interactions may be impractical in many\nreal-world applications, such as robotics, and many practical systems have to\nobey limitations in the form of state space or control constraints. To reduce\nthe number of system interactions while simultaneously handling constraints, we\npropose a model-based RL framework based on probabilistic Model Predictive\nControl (MPC). In particular, we propose to learn a probabilistic transition\nmodel using Gaussian Processes (GPs) to incorporate model uncertainty into\nlong-term predictions, thereby, reducing the impact of model errors. We then\nuse MPC to find a control sequence that minimises the expected long-term cost.\nWe provide theoretical guarantees for first-order optimality in the GP-based\ntransition models with deterministic approximate inference for long-term\nplanning. We demonstrate that our approach does not only achieve\nstate-of-the-art data efficiency, but also is a principled way for RL in\nconstrained environments.","url_abs":"http://arxiv.org/abs/1706.06491v2","url_pdf":"http://arxiv.org/pdf/1706.06491v2.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":"data-efficient-reinforcement-learning-with","repo_url":"https://github.com/SimonRennotte/Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"model-predictive-control","task_name":"Model Predictive Control"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"safe-reinforcement-learning","task_name":"Safe Reinforcement Learning"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.06491","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}