{"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-reinforcement-learning-with-5","title":"Hierarchical Reinforcement Learning with Timed Subgoals","arxiv_id":"2112.03100","date":"2021-12-06","proceeding":"NeurIPS 2021 12","authors":["Nico Gürtler","Dieter Büchler","Georg Martius"],"abstract":"Hierarchical reinforcement learning (HRL) holds great potential for sample-efficient learning on challenging long-horizon tasks. In particular, letting a higher level assign subgoals to a lower level has been shown to enable fast learning on difficult problems. However, such subgoal-based methods have been designed with static reinforcement learning environments in mind and consequently struggle with dynamic elements beyond the immediate control of the agent even though they are ubiquitous in real-world problems. In this paper, we introduce Hierarchical reinforcement learning with Timed Subgoals (HiTS), an HRL algorithm that enables the agent to adapt its timing to a dynamic environment by not only specifying what goal state is to be reached but also when. We discuss how communicating with a lower level in terms of such timed subgoals results in a more stable learning problem for the higher level. Our experiments on a range of standard benchmarks and three new challenging dynamic reinforcement learning environments show that our method is capable of sample-efficient learning where an existing state-of-the-art subgoal-based HRL method fails to learn stable solutions.","url_abs":"https://arxiv.org/abs/2112.03100v1","url_pdf":"https://arxiv.org/pdf/2112.03100v1.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":"hierarchical-reinforcement-learning-with-5","repo_url":"https://github.com/martius-lab/hits","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"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":"https://app.syntology.ai/?focus=2112.03100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.03100"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/martius-lab/hits","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"de80a6d3608aca77","entry":"design_agent_and_env","repo":"martius-lab/hits","repo_kind":"official","path":"hac_envs/hac_envs/design_agent_and_env.py","file_url":"https://github.com/martius-lab/hits/blob/HEAD/hac_envs/hac_envs/design_agent_and_env.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"de80a6d3608aca77"}},{"code_sha256_prefix":"60f6a0d68c5a85ce","entry":"design_agent_and_env","repo":"martius-lab/hits","repo_kind":"official","path":"hac_envs/hac_envs/example_designs/PENDULUM_LAY_2_design_agent_and_env.py","file_url":"https://github.com/martius-lab/hits/blob/HEAD/hac_envs/hac_envs/example_designs/PENDULUM_LAY_2_design_agent_and_env.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"60f6a0d68c5a85ce"}},{"code_sha256_prefix":"d01652b9dfddbcc8","entry":"design_agent_and_env","repo":"martius-lab/hits","repo_kind":"official","path":"hac_envs/hac_envs/ant_environments/ant_four_rooms_3_levels/design_agent_and_env.py","file_url":"https://github.com/martius-lab/hits/blob/HEAD/hac_envs/hac_envs/ant_environments/ant_four_rooms_3_levels/design_agent_and_env.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d01652b9dfddbcc8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}