{"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/hierarchical-meta-reinforcement-learning-for","title":"Hierarchical Meta Reinforcement Learning for Multi-Task Environments","arxiv_id":null,"date":"2021-01-01","proceeding":null,"authors":["Dongyang Zhao","Yue Huang","Changnan Xiao","Yue Li","Shihong Deng"],"abstract":"Deep reinforcement learning algorithms aim to achieve human-level intelligence by solving practical decisions-making problems, which are often composed of multiple sub-tasks. Complex and subtle relationships between sub-tasks make traditional methods hard to give a promising solution. We implement a first-person shooting environment with random spatial structures to illustrate a typical representative of this kind. A desirable agent should be capable of balancing between different sub-tasks: navigation to find enemies and shooting to kill them. To address the problem brought by the environment, we propose a Meta Soft Hierarchical reinforcement learning framework (MeSH), in which each low-level sub-policy focuses on a specific sub-task respectively and high-level policy automatically learns to utilize low-level sub-policies through meta-gradients. The proposed framework is able to disentangle multiple sub-tasks and discover proper low-level policies under different situations. The effectiveness and efficiency of the framework are shown by a series of comparison experiments. Both environment and algorithm code will be provided for open source to encourage further research.","url_abs":"https://openreview.net/forum?id=u9ax42K7ND","url_pdf":"https://openreview.net/pdf?id=u9ax42K7ND","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":"hierarchical-meta-reinforcement-learning-for","repo_url":"https://github.com/MeSH-ICLR/MEtaSoftHierarchy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"hierarchical-reinforcement-learning","task_name":"Hierarchical Reinforcement Learning"},{"task_slug":"meta-reinforcement-learning","task_name":"Meta 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":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}