{"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/barc-backward-reachability-curriculum-for","title":"BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning","arxiv_id":"1806.06161","date":"2018-06-16","proceeding":null,"authors":["Boris Ivanovic","James Harrison","Apoorva Sharma","Mo Chen","Marco Pavone"],"abstract":"Model-free Reinforcement Learning (RL) offers an attractive approach to learn\ncontrol policies for high-dimensional systems, but its relatively poor sample\ncomplexity often forces training in simulated environments. Even in simulation,\ngoal-directed tasks whose natural reward function is sparse remain intractable\nfor state-of-the-art model-free algorithms for continuous control. The\nbottleneck in these tasks is the prohibitive amount of exploration required to\nobtain a learning signal from the initial state of the system. In this work, we\nleverage physical priors in the form of an approximate system dynamics model to\ndesign a curriculum scheme for a model-free policy optimization algorithm. Our\nBackward Reachability Curriculum (BaRC) begins policy training from states that\nrequire a small number of actions to accomplish the task, and expands the\ninitial state distribution backwards in a dynamically-consistent manner once\nthe policy optimization algorithm demonstrates sufficient performance. BaRC is\ngeneral, in that it can accelerate training of any model-free RL algorithm on a\nbroad class of goal-directed continuous control MDPs. Its curriculum strategy\nis physically intuitive, easy-to-tune, and allows incorporating physical priors\nto accelerate training without hindering the performance, flexibility, and\napplicability of the model-free RL algorithm. We evaluate our approach on two\nrepresentative dynamic robotic learning problems and find substantial\nperformance improvement relative to previous curriculum generation techniques\nand naive exploration strategies.","url_abs":"http://arxiv.org/abs/1806.06161v2","url_pdf":"http://arxiv.org/pdf/1806.06161v2.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":"barc-backward-reachability-curriculum-for","repo_url":"https://github.com/StanfordASL/BaRC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"continuous-control","task_name":"Continuous Control"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"continuous-control","task_name":"continuous-control"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.06161","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.06161"}},"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/StanfordASL/BaRC","reach":null}],"summary":{"ran_honours":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"ran":1,"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":"076539ecce358536","entry":"sample_from_backward_reachable_set","repo":"StanfordASL/BaRC","repo_kind":"official","path":"code/curriculum.py","file_url":"https://github.com/StanfordASL/BaRC/blob/HEAD/code/curriculum.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"076539ecce358536"}},{"code_sha256_prefix":"297757dbc3099647","entry":"backward_reachable","repo":"StanfordASL/BaRC","repo_kind":"official","path":"code/curriculum.py","file_url":"https://github.com/StanfordASL/BaRC/blob/HEAD/code/curriculum.py","link_basis":"first_harvest_node","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":"297757dbc3099647"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}