End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances

2019-11-25 · CVPR 2020 6 · Marin Toromanoff, Emilie Wirbel, Fabien Moutarde

Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own experiments and not rule-based control methods. However, there is no RL algorithm yet capable of handling a task as difficult as urban driving. We present a novel technique, coined implicit affordances, to effectively leverage RL for urban driving thus including lane keeping, pedestrians and vehicles avoidance, and traffic light detection. To our knowledge we are the first to present a successful RL agent handling such a complex task especially regarding the traffic light detection. Furthermore, we have demonstrated the effectiveness of our method by winning the Camera Only track of the CARLA challenge.

📄 PDF Abstract BibTeX arXiv:1911.10868

Code (1)

valeoai/LearningByCheating 공식 구현 pytorch

Tasks

Autonomous Driving reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

Entropy Regularization 설명 없음

PPO Proximal Policy Optimization, or PPO, is a policy gradient method for reinforcement learning. The motivation was to have an algorithm with the data efficiency and reliable…

CARLA CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban…

MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning

2026-05-11 · Guangli Chen, Dianzhao Li, Wenjian Zhong, Bangquan Xie 외 arxiv

Robust urban autonomous driving requires reliable 3D scene understanding and stable decision-making under dense interactions. However, existing end-to-end models lack interpretability, while modular pipelines suffer from…

Zero-shot Generalization Reinforcement Learning Scene Understanding Autonomous Driving

Model-free Deep Reinforcement Learning for Urban Autonomous Driving

2019-04-20 · Jianyu Chen, Bodi Yuan, Masayoshi Tomizuka

Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. Current decision making methods are mostly manually designing the driving policy, which might result in s…

Autonomous Driving Decision Making Deep Reinforcement Learning reinforcement-learning+2

Affordance-based Reinforcement Learning for Urban Driving

2021-01-15 · Tanmay Agarwal, Hitesh Arora, Jeff Schneider

Traditional autonomous vehicle pipelines that follow a modular approach have been very successful in the past both in academia and industry, which has led to autonomy deployed on road. Though this approach provides ease …

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving

2022-02-17 · Yinuo Zhao, Kun Wu, Zhiyuan Xu, Zhengping Che 외

Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors. Widely-applied methods either heavily rely on hand-crafted r…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision

2018-08-30 · Ashish Mehta, Adithya Subramanian, Anbumani Subramanian

Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which uses supervised auxiliary task predictio…

Autonomous Driving Multi-Task Learning