Learning-based vehicle planning is receiving increasing attention with the emergence of
diverse driving simulators and large-scale driving datasets. While offline reinforcement …

When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …

Offline reinforcement learning (RL) provides a framework for learning decision-making from
offline data and therefore constitutes a promising approach for real-world applications as …

Reinforcement learning (RL) has shown to reach super human-level performance across a
wide range of tasks. However, unlike supervised machine learning, learning strategies that …

Since traditional reinforcement learning (RL) approaches need active online interaction with
the environment, previous works are mainly investigated in the simulation environment …

Despite promising progress in reinforcement learning (RL), developing algorithms for
autonomous driving (AD) remains challenging: one of the critical issues being the absence …

Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …

Heated debates continue over the best autonomous driving framework. The classic modular
pipeline is widely adopted in the industry owing to its great interpretability and stability …

Reinforcement Learning from Demonstrations (RLfD) has emerged as an effective method
by fusing expert demonstrations into Reinforcement Learning (RL) training, harnessing the …

Safe reinforcement learning (RL) has achieved significant success on risk-sensitive tasks
and shown promise in autonomous driving (AD) as well. Considering the distinctiveness of …