Goal-constrained Sparse Reinforcement Learning for End-to-End Driving

2021-03-16 · Pranav Agarwal, Pierre de Beaucorps, Raoul de Charette

Deep reinforcement Learning for end-to-end driving is limited by the need of complex reward engineering. Sparse rewards can circumvent this challenge but suffers from long training time and leads to sub-optimal policy. In this work, we explore full-control driving with only goal-constrained sparse reward and propose a curriculum learning approach for end-to-end driving using only navigation view maps that benefit from small virtual-to-real domain gap. To address the complexity of multiple driving policies, we learn concurrent individual policies selected at inference by a navigation system. We demonstrate the ability of our proposal to generalize on unseen road layout, and to drive significantly longer than in the training.

📄 PDF Abstract BibTeX arXiv:2103.09189

Code (0)

등록된 구현이 없습니다.

Tasks

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

CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving

2024-06-13 · Jonathan Booher, Khashayar Rohanimanesh, Junhong Xu, Vladislav Isenbaev 외

Modern approaches to autonomous driving rely heavily on learned components trained with large amounts of human driving data via imitation learning. However, these methods require large amounts of expensive data collectio…

Autonomous Driving Autonomous Vehicles Imitation Learning reinforcement-learning+3

CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving

2025-03-28 · Xinwei Gao, Arambam James Singh, Gangadhar Royyuru, Michael Yuhas 외

Lane keeping in autonomous driving systems requires scenario-specific weight tuning for different objectives. We formulate lane-keeping as a constrained reinforcement learning problem, where weight coefficients are autom…

Autonomous Driving reinforcement-learning Reinforcement Learning

Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Driving

2026-01-05 · Qi Wei, Junchao Fan, Zhao Yang, Jianhua Wang 외 arxiv

Reinforcement learning (RL) has shown considerable potential in autonomous driving (AD), yet its vulnerability to perturbations remains a critical barrier to real-world deployment. As a primary countermeasure, adversaria…

Reinforcement Learning Autonomous Driving

CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving

2018-07-10 · ECCV 2018 9 · Xiaodan Liang, Tairui Wang, Luona Yang, Eric Xing

Autonomous urban driving navigation with complex multi-agent dynamics is under-explored due to the difficulty of learning an optimal driving policy. The traditional modular pipeline heavily relies on hand-designed rules …

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

Learning to Drive Using Sparse Imitation Reinforcement Learning

2022-05-24 · Yuci Han, Alper Yilmaz

In this paper, we propose Sparse Imitation Reinforcement Learning (SIRL), a hybrid end-to-end control policy that combines the sparse expert driving knowledge with reinforcement learning (RL) policy for autonomous drivin…

Autonomous Driving reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1