{"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/environments-for-lifelong-reinforcement","title":"Environments for Lifelong Reinforcement Learning","arxiv_id":"1811.10732","date":"2018-11-26","proceeding":null,"authors":["Khimya Khetarpal","Shagun Sodhani","Sarath Chandar","Doina Precup"],"abstract":"To achieve general artificial intelligence, reinforcement learning (RL)\nagents should learn not only to optimize returns for one specific task but also\nto constantly build more complex skills and scaffold their knowledge about the\nworld, without forgetting what has already been learned. In this paper, we\ndiscuss the desired characteristics of environments that can support the\ntraining and evaluation of lifelong reinforcement learning agents, review\nexisting environments from this perspective, and propose recommendations for\ndevising suitable environments in the future.","url_abs":"http://arxiv.org/abs/1811.10732v2","url_pdf":"http://arxiv.org/pdf/1811.10732v2.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":"environments-for-lifelong-reinforcement","repo_url":"https://github.com/erfanMhi/awesome-continual-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"environments-for-lifelong-reinforcement","repo_url":"https://github.com/erfanMhi/awesome-continual-representation-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"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":"https://syntology.ai/paper/1811.10732","atlas_url":"https://app.syntology.ai/?focus=1811.10732","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}