Reinforcement Learning (RL) algorithms show amazing performance in recent years, but
placing RL in real-world applications such as self-driven vehicles may suffer 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 …

Reinforcement learning (RL) has been widely used in decision-making tasks, but it cannot
guarantee the agent's safety in the training process due to the requirements of interaction …

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 achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

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 …

Autonomous agents are playing an ever-increasing role, including in automated driving
vehicles or house-care robots. When an autonomous agent interacts with humans, one of …

Recently, safe reinforcement learning (RL) with the actor-critic structure for continuous
control tasks has received increasing attention. It is still challenging to learn a near-optimal …

Reinforcement learning (RL) has become a prominent research area in recent years due to
its ability to address decision-making problems by maximizing cumulative rewards in …

Reinforcement learning (RL) has been successfully applied to sequential decision-making
problems, eg, playing computer games or solving robotic tasks in simulations. However, RL …