Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by
learning policies from pre-collected datasets without interacting with the environment …

Exploration remains a critical issue in deep reinforcement learning for an agent to attain high
returns in unknown environments. Although the prevailing exploration Random Network …

Nuclear energy plays a crucial role in energy supply in the 21st century, and more and more
Nuclear Power Plants (NPPs) will be in operation to contribute to the development of human …

Reinforcement learning (RL) is increasingly applied in recommending ventilator parameters,
yet existing methods prioritize therapeutic effect over patient safety. This leads to excessive …

Visual reinforcement learning has proven effective in solving control tasks with high-
dimensional observations. However, extracting reliable and generalizable representations …

Equipped with the trained environmental dynamics, model-based offline reinforcement
learning (RL) algorithms can often successfully learn good policies from fixed-sized …

Offline reinforcement learning (RL) aims to optimize policy using collected data without
online interactions. Model-based approaches are particularly appealing for addressing …

Model-based offline reinforcement learning (RL) has made remarkable progress, offering a
promising avenue for improving generalization with synthetic model rollouts. Existing works …

Offline reinforcement learning (RL) has attracted much attention due to its ability in learning
from static offline datasets and eliminating the need of interacting with the environment …

Offline reinforcement learning (RL) aims to learn policies entirely from passively collected
datasets, making it a data‐driven decision method. One of the main challenges in offline RL …