{"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/the-streetlearn-environment-and-dataset","title":"The StreetLearn Environment and Dataset","arxiv_id":"1903.01292","date":"2019-03-04","proceeding":null,"authors":["Piotr Mirowski","Andras Banki-Horvath","Keith Anderson","Denis Teplyashin","Karl Moritz Hermann","Mateusz Malinowski","Matthew Koichi Grimes","Karen Simonyan","Koray Kavukcuoglu","Andrew Zisserman","Raia Hadsell"],"abstract":"Navigation is a rich and well-grounded problem domain that drives progress in\nmany different areas of research: perception, planning, memory, exploration,\nand optimisation in particular. Historically these challenges have been\nseparately considered and solutions built that rely on stationary datasets -\nfor example, recorded trajectories through an environment. These datasets\ncannot be used for decision-making and reinforcement learning, however, and in\ngeneral the perspective of navigation as an interactive learning task, where\nthe actions and behaviours of a learning agent are learned simultaneously with\nthe perception and planning, is relatively unsupported. Thus, existing\nnavigation benchmarks generally rely on static datasets (Geiger et al., 2013;\nKendall et al., 2015) or simulators (Beattie et al., 2016; Shah et al., 2018).\nTo support and validate research in end-to-end navigation, we present\nStreetLearn: an interactive, first-person, partially-observed visual\nenvironment that uses Google Street View for its photographic content and broad\ncoverage, and give performance baselines for a challenging goal-driven\nnavigation task. The environment code, baseline agent code, and the dataset are\navailable at http://streetlearn.cc","url_abs":"http://arxiv.org/abs/1903.01292v1","url_pdf":"http://arxiv.org/pdf/1903.01292v1.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":"the-streetlearn-environment-and-dataset","repo_url":"https://github.com/deepmind/streetlearn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"the-streetlearn-environment-and-dataset","repo_url":"https://github.com/google-deepmind/streetlearn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"cross-view-geo-localisation","task_name":"Cross-View Geo-Localisation"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[],"datasets_introduced":[{"slug":"streetlearn","name":"StreetLearn","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1903.01292","atlas_url":"https://app.syntology.ai/?focus=1903.01292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.01292"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. 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