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

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

Safe reinforcement learning (RL) is crucial for deploying RL agents in real-world
applications, as it aims to maximize long-term rewards while satisfying safety constraints …

Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …

Abstract Machine Learning (ML) techniques, including Reinforcement Learning (RL),
demonstrate potential as decision-making controllers. However, enhancing the robustness …

The success of deep reinforcement learning (DRL) relies on the availability and quality of
training data, often requiring extensive interactions with specific environments. In many real …

With the widespread use of reinforcement learning (RL) in the energy system, the damage to
the system caused by the agents' stochastic exploration is beginning to be appreciated. We …

The success of deep reinforcement learning (DRL) hinges on the availability of training data,
which is typically obtained via a large number of environment interactions. In many real …