{"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/look-before-you-leap-bridging-model-free-and","title":"Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation","arxiv_id":"1803.07729","date":"2018-03-21","proceeding":"ECCV 2018 9","authors":["Xin Wang","Wenhan Xiong","Hongmin Wang","William Yang Wang"],"abstract":"Existing research studies on vision and language grounding for robot\nnavigation focus on improving model-free deep reinforcement learning (DRL)\nmodels in synthetic environments. However, model-free DRL models do not\nconsider the dynamics in the real-world environments, and they often fail to\ngeneralize to new scenes. In this paper, we take a radical approach to bridge\nthe gap between synthetic studies and real-world practices---We propose a\nnovel, planned-ahead hybrid reinforcement learning model that combines\nmodel-free and model-based reinforcement learning to solve a real-world\nvision-language navigation task. Our look-ahead module tightly integrates a\nlook-ahead policy model with an environment model that predicts the next state\nand the reward. Experimental results suggest that our proposed method\nsignificantly outperforms the baselines and achieves the best on the real-world\nRoom-to-Room dataset. Moreover, our scalable method is more generalizable when\ntransferring to unseen environments.","url_abs":"http://arxiv.org/abs/1803.07729v2","url_pdf":"http://arxiv.org/pdf/1803.07729v2.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":"look-before-you-leap-bridging-model-free-and","repo_url":"https://github.com/peteanderson80/Matterport3DSimulator","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"model-based-reinforcement-learning","task_name":"Model-based Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"robot-navigation","task_name":"Robot Navigation"},{"task_slug":"vision-and-language-navigation","task_name":"Vision and Language Navigation"},{"task_slug":"vision-language-navigation","task_name":"Vision-Language Navigation"},{"task_slug":"model","task_name":"model"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.07729","atlas_url":"https://app.syntology.ai/?focus=1803.07729","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}