{"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/diversity-driven-extensible-hierarchical","title":"Diversity-Driven Extensible Hierarchical Reinforcement Learning","arxiv_id":"1811.04324","date":"2018-11-10","proceeding":null,"authors":["Yuhang Song","Jianyi Wang","Thomas Lukasiewicz","Zhenghua Xu","Mai Xu"],"abstract":"Hierarchical reinforcement learning (HRL) has recently shown promising\nadvances on speeding up learning, improving the exploration, and discovering\nintertask transferable skills. Most recent works focus on HRL with two levels,\ni.e., a master policy manipulates subpolicies, which in turn manipulate\nprimitive actions. However, HRL with multiple levels is usually needed in many\nreal-world scenarios, whose ultimate goals are highly abstract, while their\nactions are very primitive. Therefore, in this paper, we propose a\ndiversity-driven extensible HRL (DEHRL), where an extensible and scalable\nframework is built and learned levelwise to realize HRL with multiple levels.\nDEHRL follows a popular assumption: diverse subpolicies are useful, i.e.,\nsubpolicies are believed to be more useful if they are more diverse. However,\nexisting implementations of this diversity assumption usually have their own\ndrawbacks, which makes them inapplicable to HRL with multiple levels.\nConsequently, we further propose a novel diversity-driven solution to achieve\nthis assumption in DEHRL. Experimental studies evaluate DEHRL with five\nbaselines from four perspectives in two domains; the results show that DEHRL\noutperforms the state-of-the-art baselines in all four aspects.","url_abs":"http://arxiv.org/abs/1811.04324v2","url_pdf":"http://arxiv.org/pdf/1811.04324v2.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":"diversity-driven-extensible-hierarchical","repo_url":"https://github.com/YuhangSong/DEHRL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"hierarchical-reinforcement-learning","task_name":"Hierarchical 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":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}