Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen
driving scenarios, largely stemming from the non-interpretability and poor generalization of …

Reinforcement learning has shown promising results in learning neural network policies for
complicated control tasks. However, the lack of formal guarantees about the behavior of …

Safe offline RL is a promising way to bypass risky online interactions towards safe policy
learning. Most existing methods only enforce soft constraints, ie, constraining safety …

Safe exploration is essential for the practical use of reinforcement learning (RL) in many real-
world scenarios. In this paper, we present a generalized safe exploration (GSE) problem as …

In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the
challenges of balancing the tradeoff between maximizing rewards and minimizing safety …

We investigate a novel safe reinforcement learning problem with step-wise violation
constraints. Our problem differs from existing works in that we focus on stricter step-wise …

The rapid advancement of edge and cloud computing platforms, vehicular ad-hoc networks,
and machine learning techniques have brought both opportunities and challenges for next …