Safety is one of the main challenges in applying reinforcement learning to tasks in realistic
environments. To ensure safety during and after the training process, existing methods tend …

Reinforcement learning (RL) holds the promise of autonomous robots because it can adapt
to dynamic or unknown environments by automatically learning optimal control policies from …

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 …

Safety controllers is widely used to achieve safe reinforcement learning. Most methods that
apply a safety controller are using handcrafted safety constraints to construct the safety …

Reinforcement learning (RL) algorithms can achieve state-of-the-art performance in decision-
making and continuous control tasks. However, applying RL algorithms on safety-critical …

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 …

As safety violations can lead to severe consequences in real-world robotic applications, the
increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the …

Safe reinforcement learning is a promising path toward applying reinforcement learning
algorithms to real-world problems, where suboptimal behaviors may lead to actual negative …

In reinforcement learning (RL), exploration is essential to achieve a globally optimal policy
but unconstrained exploration can cause damages to robots and nearby people. To handle …

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 …