{"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/accuracy-based-curriculum-learning-in-deep","title":"Accuracy-based Curriculum Learning in Deep Reinforcement Learning","arxiv_id":"1806.09614","date":"2018-06-25","proceeding":null,"authors":["Pierre Fournier","Olivier Sigaud","Mohamed Chetouani","Pierre-Yves Oudeyer"],"abstract":"In this paper, we investigate a new form of automated curriculum learning\nbased on adaptive selection of accuracy requirements, called accuracy-based\ncurriculum learning. Using a reinforcement learning agent based on the Deep\nDeterministic Policy Gradient algorithm and addressing the Reacher environment,\nwe first show that an agent trained with various accuracy requirements sampled\nrandomly learns more efficiently than when asked to be very accurate at all\ntimes. Then we show that adaptive selection of accuracy requirements, based on\na local measure of competence progress, automatically generates a curriculum\nwhere difficulty progressively increases, resulting in a better learning\nefficiency than sampling randomly.","url_abs":"http://arxiv.org/abs/1806.09614v2","url_pdf":"http://arxiv.org/pdf/1806.09614v2.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":"accuracy-based-curriculum-learning-in-deep","repo_url":"https://github.com/allanpichardo/dontpanic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"accuracy-based-curriculum-learning-in-deep","repo_url":"https://github.com/fabian57fabian/MinGrid-Improved-RL-Methods","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"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":"https://app.syntology.ai/?focus=1806.09614","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}