{"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/magic-learning-macro-actions-for-online-pomdp","title":"MAGIC: Learning Macro-Actions for Online POMDP Planning","arxiv_id":"2011.03813","date":"2020-11-07","proceeding":null,"authors":["Yiyuan Lee","Panpan Cai","David Hsu"],"abstract":"The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computational complexity, when long-term planning is required. While temporally-extended macro-actions help to cut down the effective planning horizon and significantly improve computational efficiency, how do we acquire good macro-actions? This paper proposes Macro-Action Generator-Critic (MAGIC), which performs offline learning of macro-actions optimized for online POMDP planning. Specifically, MAGIC learns a macro-action generator end-to-end, using an online planner's performance as the feedback. During online planning, the generator generates on the fly situation-aware macro-actions conditioned on the robot's belief and the environment context. We evaluated MAGIC on several long-horizon planning tasks both in simulation and on a real robot. The experimental results show that the learned macro-actions offer significant benefits in online planning performance, compared with primitive actions and handcrafted macro-actions.","url_abs":"https://arxiv.org/abs/2011.03813v4","url_pdf":"https://arxiv.org/pdf/2011.03813v4.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":"magic-learning-macro-actions-for-online-pomdp","repo_url":"https://github.com/AdaCompNUS/magic","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"decision-making-under-uncertainty","task_name":"Decision Making Under Uncertainty"}],"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}