Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the
environment. We aim to tackle a more challenging problem: learning a safe policy from an …

While real-world applications of reinforcement learning (RL) are becoming popular, the
security and robustness of RL systems are worthy of more attention and exploration. In …

Code models, such as CodeBERT and CodeT5, offer general-purpose representations of
code and play a vital role in supporting downstream automated software engineering tasks …

This paper systematically analyzes the security risks associated with artificial intelligence
(AI) components in autonomous vehicles (AVs). Given the increasing reliance on AI for …

Artificial Intelligence (AI) systems, which benefit from the availability of large-scale datasets
and increasing computational power, have become effective solutions to various critical …

Backdoor attacks in reinforcement learning (RL) have previously employed intense attack
strategies to ensure attack success. However, these methods suffer from high attack costs …

The combination of deep reinforcement learning (DRL) with ensemble methods has been
proved to be highly effective in addressing complex sequential decision-making problems …

The emergence of on-demand ride pooling services allows each vehicle to serve multiple
passengers at a time, thus increasing drivers' income and enabling passengers to travel at …

Electricity demands are increasing significantly and the traditional power grid system is
facing huge challenges. As the desired next-generation power grid system, smart grid can …

Data-driven neural path planners are attracting increasing interest in the robotics
community. However, their neural network components typically come as black boxes …