Reinforcement Learning (RL) algorithms have achieved remarkable performance in
decision making and control tasks by reasoning about long-term, cumulative reward using …

Reinforcement learning (RL) is capable of sophisticated motion planning and control for
robots in uncertain environments. However, state-of-the-art deep RL approaches typically …

Safety remains a central obstacle preventing widespread use of RL in the real world:
learning new tasks in uncertain environments requires extensive exploration, but safety …

Reinforcement Learning (RL) has been shown to be effective in many scenarios. However, it
typically requires the exploration of a sufficiently large number of state-action pairs, some of …

Reinforcement learning for robotic applications faces the challenge of constraint satisfaction,
which currently impedes its application to safety critical systems. Recent approaches …

Safety comes first in many real-world applications involving autonomous agents. Despite a
large number of reinforcement learning (RL) methods focusing on safety-critical tasks, there …

Safety is a critical concern when applying reinforcement learning (RL) to real-world control
tasks. However, existing safe RL works either only consider expected safety constraint …

Reinforcement Learning (RL) algorithms have found limited success beyond simulated
applications, and one main reason is the absence of safety guarantees during the learning …

Reinforcement Learning (RL) is a widely employed machine learning architecture that has
been applied to a variety of decision-making problems, from resource management to robot …

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 …