model-free7 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. Addressing Function Approximation Error in Actor-Critic Methods (TD3) https://arxiv.org/abs/1802.09477 Addressing Function Approximation Error in Actor-Critic Methods In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel arxiv.org Concepts "Twin Delayed Deep Determ.. 2024. 1. 31. Asynchronous Methods for Deep Reinforcement Learning (A3C) https://arxiv.org/abs/1602.01783 Asynchronous Methods for Deep Reinforcement Learning We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning arxiv.org Concepts 본 논문에서는 기존 off-policy method들의 memor.. 2024. 1. 30. 이전 1 2 다음
