{"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/target-driven-visual-navigation-in-indoor","title":"Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning","arxiv_id":"1609.05143","date":"2016-09-16","proceeding":null,"authors":["Yuke Zhu","Roozbeh Mottaghi","Eric Kolve","Joseph J. Lim","Abhinav Gupta","Li Fei-Fei","Ali Farhadi"],"abstract":"Two less addressed issues of deep reinforcement learning are (1) lack of\ngeneralization capability to new target goals, and (2) data inefficiency i.e.,\nthe model requires several (and often costly) episodes of trial and error to\nconverge, which makes it impractical to be applied to real-world scenarios. In\nthis paper, we address these two issues and apply our model to the task of\ntarget-driven visual navigation. To address the first issue, we propose an\nactor-critic model whose policy is a function of the goal as well as the\ncurrent state, which allows to better generalize. To address the second issue,\nwe propose AI2-THOR framework, which provides an environment with high-quality\n3D scenes and physics engine. Our framework enables agents to take actions and\ninteract with objects. Hence, we can collect a huge number of training samples\nefficiently.\n  We show that our proposed method (1) converges faster than the\nstate-of-the-art deep reinforcement learning methods, (2) generalizes across\ntargets and across scenes, (3) generalizes to a real robot scenario with a\nsmall amount of fine-tuning (although the model is trained in simulation), (4)\nis end-to-end trainable and does not need feature engineering, feature matching\nbetween frames or 3D reconstruction of the environment.\n  The supplementary video can be accessed at the following link:\nhttps://youtu.be/SmBxMDiOrvs.","url_abs":"http://arxiv.org/abs/1609.05143v1","url_pdf":"http://arxiv.org/pdf/1609.05143v1.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":"target-driven-visual-navigation-in-indoor","repo_url":"https://github.com/CAVED123/TD-DPPO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"target-driven-visual-navigation-in-indoor","repo_url":"https://github.com/shamanez/Target-Driven-Visual-Navigation-with-Distributed-PPO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-reconstruction","task_name":"3D Reconstruction"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"visual-navigation","task_name":"Visual Navigation"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.05143","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}