{"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/teaching-a-machine-to-read-maps-with-deep","title":"Teaching a Machine to Read Maps with Deep Reinforcement Learning","arxiv_id":"1711.07479","date":"2017-11-20","proceeding":null,"authors":["Gino Brunner","Oliver Richter","Yuyi Wang","Roger Wattenhofer"],"abstract":"The ability to use a 2D map to navigate a complex 3D environment is quite\nremarkable, and even difficult for many humans. Localization and navigation is\nalso an important problem in domains such as robotics, and has recently become\na focus of the deep reinforcement learning community. In this paper we teach a\nreinforcement learning agent to read a map in order to find the shortest way\nout of a random maze it has never seen before. Our system combines several\nstate-of-the-art methods such as A3C and incorporates novel elements such as a\nrecurrent localization cell. Our agent learns to localize itself based on 3D\nfirst person images and an approximate orientation angle. The agent generalizes\nwell to bigger mazes, showing that it learned useful localization and\nnavigation capabilities.","url_abs":"http://arxiv.org/abs/1711.07479v1","url_pdf":"http://arxiv.org/pdf/1711.07479v1.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":"teaching-a-machine-to-read-maps-with-deep","repo_url":"https://github.com/OliverRichter/map-reader","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"navigate","task_name":"Navigate"},{"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":[{"method_slug":"a3c","method_name":"A3C"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"entropy-regularization","method_name":"Entropy Regularization"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}