{"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/reinforced-continual-learning","title":"Reinforced Continual Learning","arxiv_id":"1805.12369","date":"2018-05-31","proceeding":"NeurIPS 2018 12","authors":["Ju Xu","Zhanxing Zhu"],"abstract":"Most artificial intelligence models have limiting ability to solve new tasks\nfaster, without forgetting previously acquired knowledge. The recently emerging\nparadigm of continual learning aims to solve this issue, in which the model\nlearns various tasks in a sequential fashion. In this work, a novel approach\nfor continual learning is proposed, which searches for the best neural\narchitecture for each coming task via sophisticatedly designed reinforcement\nlearning strategies. We name it as Reinforced Continual Learning. Our method\nnot only has good performance on preventing catastrophic forgetting but also\nfits new tasks well. The experiments on sequential classification tasks for\nvariants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach\noutperforms existing continual learning alternatives for deep networks.","url_abs":"http://arxiv.org/abs/1805.12369v1","url_pdf":"http://arxiv.org/pdf/1805.12369v1.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":"reinforced-continual-learning","repo_url":"https://github.com/xujinfan/Reinforced-Continual-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"classification","task_name":"General Classification"},{"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":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1805.12369","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}