Reinforcement Learning (RL) has become an increasingly important research area as the
success of machine learning algorithms and methods grows. To combat the safety concerns …

Reinforcement Learning (RL) has shown remarkable success in solving relatively complex
tasks, yet the deployment of RL systems in real-world scenarios poses significant challenges …

Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-
critical real-world applications, such as autonomous driving, human-robot interaction, robot …

Reinforcement learning (RL) is emerging as a powerful machine learning paradigm to
develop autonomous safety critical systems; RL enables the systems to learn optimal control …

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

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

Reinforcement Learning (RL) provides an ability to train an artificial intelligent agent in
dynamic and uncertain environments. RL has demonstrated an impressive performance …

Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their
potential for many real-world tasks. However, vanilla RL and most safe RL approaches do …

Reinforcement learning (RL) has been an attractive potential for designing autonomous
systems due to its learning-by-exploration approach. However, this learning process makes …

Despite recent advances in safe reinforcement learning (RL), safety constraints are often
violated at deployment, especially under extreme uncertainty in memory-based partially …