{"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/gri-general-reinforced-imitation-and-its","title":"GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving","arxiv_id":"2111.08575","date":"2021-11-16","proceeding":null,"authors":["Raphael Chekroun","Marin Toromanoff","Sascha Hornauer","Fabien Moutarde"],"abstract":"Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications such as autonomous driving and robotics. However, DRL is notoriously limited by its high sample complexity and its lack of stability. Prior knowledge, e.g. as expert demonstrations, is often available but challenging to leverage to mitigate these issues. In this paper, we propose General Reinforced Imitation (GRI), a novel method which combines benefits from exploration and expert data and is straightforward to implement over any off-policy RL algorithm. We make one simplifying hypothesis: expert demonstrations can be seen as perfect data whose underlying policy gets a constant high reward. Based on this assumption, GRI introduces the notion of offline demonstration agents. This agent sends expert data which are processed both concurrently and indistinguishably with the experiences coming from the online RL exploration agent. We show that our approach enables major improvements on vision-based autonomous driving in urban environments. We further validate the GRI method on Mujoco continuous control tasks with different off-policy RL algorithms. Our method ranked first on the CARLA Leaderboard and outperforms World on Rails, the previous state-of-the-art, by 17%.","url_abs":"https://arxiv.org/abs/2111.08575v2","url_pdf":"https://arxiv.org/pdf/2111.08575v2.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":[],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"carla-map-leaderboard","task_name":"CARLA MAP Leaderboard"},{"task_slug":"continuous-control","task_name":"Continuous Control"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"mujoco","task_name":"MuJoCo"},{"task_slug":"continuous-control","task_name":"continuous-control"}],"methods":[{"method_slug":"carla","method_name":"CARLA"},{"method_slug":"entropy-regularization","method_name":"Entropy Regularization"},{"method_slug":"ppo","method_name":"PPO"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/autonomous-driving-on-carla-leaderboard","task":"Autonomous Driving","dataset":"CARLA Leaderboard","model":"GRIAD","rank_in_archive_order":10,"of":18,"metrics":{"Driving Score":"36.79","Infraction penalty":"0.6","Route Completion":"61.85"},"uses_additional_data":false},{"leaderboard":"/sota/carla-map-leaderboard-on-carla","task":"CARLA MAP Leaderboard","dataset":"CARLA","model":"GRI-based DRL","rank_in_archive_order":4,"of":8,"metrics":{"Driving score":"33.785","Infraction penalty":"0.568","Route completion":"57.442"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2111.08575","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}