A survey of deep RL and IL for autonomous driving policy learning

Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

Deep reinforcement learning for autonomous driving: A survey

BR Kiran, I Sobh, V Talpaert, P Mannion… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

Exploring applications of deep reinforcement learning for real-world autonomous driving systems

V Talpaert, I Sobh, BR Kiran, P Mannion… - arXiv preprint arXiv …, 2019 - arxiv.org
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years,
with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed …

Improved deep reinforcement learning with expert demonstrations for urban autonomous driving

H Liu, Z Huang, J Wu, C Lv - 2022 IEEE intelligent vehicles …, 2022 - ieeexplore.ieee.org
Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …

Towards robust decision-making for autonomous driving on highway

K Yang, X Tang, S Qiu, S Jin, Z Wei… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …

Efficient deep reinforcement learning with imitative expert priors for autonomous driving

Z Huang, J Wu, C Lv - IEEE Transactions on Neural Networks …, 2022 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …

Research on autonomous driving decision-making strategies based deep reinforcement learning

Z Wang, H Yan, C Wei, J Wang, S Bo… - arXiv preprint arXiv …, 2024 - arxiv.org
The behavior decision-making subsystem is a key component of the autonomous driving
system, which reflects the decision-making ability of the vehicle and the driver, and is an …

Hierarchical program-triggered reinforcement learning agents for automated driving

B Gangopadhyay, H Soora… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Recent advances in Reinforcement Learning (RL) combined with Deep Learning (DL) have
demonstrated impressive performance in complex tasks, including autonomous driving. The …

Deep inverse reinforcement learning for behavior prediction in autonomous driving: Accurate forecasts of vehicle motion

T Fernando, S Denman, S Sridharan… - IEEE Signal …, 2020 - ieeexplore.ieee.org
Accurate behavior anticipation is essential for autonomous vehicles when navigating in
close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in …

Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning

J Chen, SE Li, M Tomizuka - IEEE Transactions on Intelligent …, 2021 - ieeexplore.ieee.org
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the
perception, decision and control problems in an integrated way, which can be more …