With extensive pretrained knowledge and high-level general capabilities, large language
models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in …

AI systems empowered by reinforcement learning (RL) algorithms harbor the immense
potential to catalyze societal advancement, yet their deployment is often impeded by …

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 …

Offline safe reinforcement learning (RL) algorithms promise to learn policies that satisfy
safety constraints directly in offline datasets without interacting with the environment. This …

Autonomous driving (AD) endows vehicles with the capability to drive partly or entirely
without human intervention. AD agents generate driving policies based on online perception …

We present a novel observation about the behavior of offline reinforcement learning (RL)
algorithms: on many benchmark datasets, offline RL can produce well-performing and safe …

This paper presents a comprehensive benchmarking suite tailored to offline safe
reinforcement learning (RL) challenges, aiming to foster progress in the development and …

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 …

Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill
learning in the form of reaching diverse goals from purely offline datasets. We propose …

We first raise and tackle a``time synchronization''issue between the agent and the
environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real …