{"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/increasing-data-efficiency-of-driving-agent","title":"Increasing Data Efficiency of Driving Agent By World Model","arxiv_id":null,"date":"2020-12-14","proceeding":"CUHK Course IERG5350 2020 12","authors":["Yicheng Liu","CAO Qianqian"],"abstract":"Reinforcement learning algorithms for real-world autonomous driving must be able to handle complex, unknown dynamical systems. This requirement is han- dled well by model-free algorithm such as PPO. However, model-free approach tend to be substantially less sample-efficient. In this work, we aim to retain the advantages of model-free method and increase the stability and data-efficiency of PPO. To this end we proposed a world model to model popular reinforcement learning environments through compressed spatio-temporal representations, which allow model-free method learning behaviors from imagined outcomes to increase sample-efficiency. The experimental results indicate that our approach mitigating the inefficiency of PPO, increasing the stability, and largely reducing the train- ing time. code is available at www.github.com/Mrmoore98/World-Model.git. The video can be found here.","url_abs":"https://openreview.net/forum?id=MxNfK_WjaQX","url_pdf":"https://openreview.net/pdf?id=MxNfK_WjaQX","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":"increasing-data-efficiency-of-driving-agent","repo_url":"https://github.com/mrmoore98/world-model","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"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":"entropy-regularization","method_name":"Entropy Regularization"},{"method_slug":"ppo","method_name":"PPO"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}