{"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/deepmind-control-suite","title":"DeepMind Control Suite","arxiv_id":"1801.00690","date":"2018-01-02","proceeding":null,"authors":["Yuval Tassa","Yotam Doron","Alistair Muldal","Tom Erez","Yazhe Li","Diego de Las Casas","David Budden","Abbas Abdolmaleki","Josh Merel","Andrew Lefrancq","Timothy Lillicrap","Martin Riedmiller"],"abstract":"The DeepMind Control Suite is a set of continuous control tasks with a\nstandardised structure and interpretable rewards, intended to serve as\nperformance benchmarks for reinforcement learning agents. The tasks are written\nin Python and powered by the MuJoCo physics engine, making them easy to use and\nmodify. We include benchmarks for several learning algorithms. The Control\nSuite is publicly available at https://www.github.com/deepmind/dm_control . A\nvideo summary of all tasks is available at http://youtu.be/rAai4QzcYbs .","url_abs":"http://arxiv.org/abs/1801.00690v1","url_pdf":"http://arxiv.org/pdf/1801.00690v1.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":"deepmind-control-suite","repo_url":"https://github.com/deepmind/dm_control","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"deepmind-control-suite","repo_url":"https://github.com/google-research/pisac","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"deepmind-control-suite","repo_url":"https://github.com/lqnew/continuous_control_benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deepmind-control-suite","repo_url":"https://github.com/nicklashansen/tdmpc2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deepmind-control-suite","repo_url":"https://github.com/ramanans1/dm_control","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deepmind-control-suite","repo_url":"https://github.com/svikramank/dm_control","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deepmind-control-suite","repo_url":"https://github.com/NervanaSystems/coach","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"deepmind-control-suite","repo_url":"https://github.com/toni-sm/skrl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"jax","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"continuous-control","task_name":"Continuous Control"},{"task_slug":"mujoco","task_name":"MuJoCo"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"continuous-control","task_name":"continuous-control"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[{"slug":"deepmind-control-suite","name":"DeepMind Control Suite","full_name":"DeepMind Control Suite"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.00690","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}