{"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/using-parameterized-black-box-priors-to-scale","title":"Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics","arxiv_id":"1709.06917","date":"2017-09-20","proceeding":null,"authors":["Konstantinos Chatzilygeroudis","Jean-Baptiste Mouret"],"abstract":"The most data-efficient algorithms for reinforcement learning in robotics are\nmodel-based policy search algorithms, which alternate between learning a\ndynamical model of the robot and optimizing a policy to maximize the expected\nreturn given the model and its uncertainties. Among the few proposed\napproaches, the recently introduced Black-DROPS algorithm exploits a black-box\noptimization algorithm to achieve both high data-efficiency and good\ncomputation times when several cores are used; nevertheless, like all\nmodel-based policy search approaches, Black-DROPS does not scale to high\ndimensional state/action spaces. In this paper, we introduce a new model\nlearning procedure in Black-DROPS that leverages parameterized black-box priors\nto (1) scale up to high-dimensional systems, and (2) be robust to large\ninaccuracies of the prior information. We demonstrate the effectiveness of our\napproach with the \"pendubot\" swing-up task in simulation and with a physical\nhexapod robot (48D state space, 18D action space) that has to walk forward as\nfast as possible. The results show that our new algorithm is more\ndata-efficient than previous model-based policy search algorithms (with and\nwithout priors) and that it can allow a physical 6-legged robot to learn new\ngaits in only 16 to 30 seconds of interaction time.","url_abs":"http://arxiv.org/abs/1709.06917v2","url_pdf":"http://arxiv.org/pdf/1709.06917v2.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":"using-parameterized-black-box-priors-to-scale","repo_url":"https://github.com/resibots/blackdrops","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"continuous-control","task_name":"Continuous Control"},{"task_slug":"reinforcement-learning","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}