논문 리뷰/Autonomous racing5 Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning https://ai.sony/publications/Outracing-champion-Gran-Turismo-drivers-with-deep-reinforcement-learning/ Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning – Sony AI Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning Peter Wurman Samuel Barrett Kenta Kawamoto James MacGlashan Kaushik Subramanian Thomas Walsh Roberto Capobianco Alisa Devlic Franziska Ecke.. 2024. 3. 2. Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning https://arxiv.org/abs/2103.14666 Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning Professional race-car drivers can execute extreme overtaking maneuvers. However, existing algorithms for autonomous overtaking either rely on simplified assumptions about the vehicle dynamics or try to solve expensive trajectory-optimization problems onlin arxiv.org Introduction 1.. 2024. 2. 17. Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation https://arxiv.org/abs/2111.06449 Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art realistic racing game AI agent that outperforms human players does no.. 2024. 2. 12. Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning https://arxiv.org/abs/2008.07971 Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning Autonomous car racing is a major challenge in robotics. It raises fundamental problems for classical approaches such as planning minimum-time trajectories under uncertain dynamics and controlling the car at the limits of its handling. Besides, the requirem arxiv.org Introduction 1. [B.. 2024. 2. 4. Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing https://arxiv.org/abs/2103.04909 Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms. While learning world models for high-dimensional observations (e.g., pixel inputs) has become practicable on standar arxiv.org Introduction 2020 ICLR에서 발표.. 2024. 2. 3. 이전 1 다음
