To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …

Reinforcement learning (RL) requires skillful definition and remarkable computational efforts
to solve optimization and control problems, which could impair its prospect. Introducing …

Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …

Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL
models typically involves in a multi-step process: pre-training RL models on simulators …

Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …

Reinforcement learning (RL) can be used to create a tactical decision-making agent for
autonomous driving. However, previous approaches only output decisions and do not …

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 …

Model information can be used to predict future trajectories, so it has huge potential to avoid
dangerous regions when applying reinforcement learning (RL) on real-world tasks, like …

Currently, the complex traffic environment challenges the fast and accurate response of a
connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to …

Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …