{"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/continuous-adaptation-via-meta-learning-in","title":"Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments","arxiv_id":"1710.03641","date":"2017-10-10","proceeding":"ICLR 2018 1","authors":["Maruan Al-Shedivat","Trapit Bansal","Yuri Burda","Ilya Sutskever","Igor Mordatch","Pieter Abbeel"],"abstract":"Ability to continuously learn and adapt from limited experience in\nnonstationary environments is an important milestone on the path towards\ngeneral intelligence. In this paper, we cast the problem of continuous\nadaptation into the learning-to-learn framework. We develop a simple\ngradient-based meta-learning algorithm suitable for adaptation in dynamically\nchanging and adversarial scenarios. Additionally, we design a new multi-agent\ncompetitive environment, RoboSumo, and define iterated adaptation games for\ntesting various aspects of continuous adaptation strategies. We demonstrate\nthat meta-learning enables significantly more efficient adaptation than\nreactive baselines in the few-shot regime. Our experiments with a population of\nagents that learn and compete suggest that meta-learners are the fittest.","url_abs":"http://arxiv.org/abs/1710.03641v2","url_pdf":"http://arxiv.org/pdf/1710.03641v2.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":"continuous-adaptation-via-meta-learning-in","repo_url":"https://github.com/openai/robosumo","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.03641","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}