{"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/learning-to-navigate-in-complex-environments","title":"Learning to Navigate in Complex Environments","arxiv_id":"1611.03673","date":"2016-11-11","proceeding":null,"authors":["Piotr Mirowski","Razvan Pascanu","Fabio Viola","Hubert Soyer","Andrew J. Ballard","Andrea Banino","Misha Denil","Ross Goroshin","Laurent SIfre","Koray Kavukcuoglu","Dharshan Kumaran","Raia Hadsell"],"abstract":"Learning to navigate in complex environments with dynamic elements is an\nimportant milestone in developing AI agents. In this work we formulate the\nnavigation question as a reinforcement learning problem and show that data\nefficiency and task performance can be dramatically improved by relying on\nadditional auxiliary tasks leveraging multimodal sensory inputs. In particular\nwe consider jointly learning the goal-driven reinforcement learning problem\nwith auxiliary depth prediction and loop closure classification tasks. This\napproach can learn to navigate from raw sensory input in complicated 3D mazes,\napproaching human-level performance even under conditions where the goal\nlocation changes frequently. We provide detailed analysis of the agent\nbehaviour, its ability to localise, and its network activity dynamics, showing\nthat the agent implicitly learns key navigation abilities.","url_abs":"http://arxiv.org/abs/1611.03673v3","url_pdf":"http://arxiv.org/pdf/1611.03673v3.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":"learning-to-navigate-in-complex-environments","repo_url":"https://github.com/deepmind/lab","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"depth-prediction","task_name":"Depth Prediction"},{"task_slug":"classification","task_name":"General Classification"},{"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":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.03673","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}