Offline reinforcement learning (offline RL) aims to find task-solving policies from prerecorded
datasets without online environment interaction. It is unfortunate that extrapolation errors can …

Offline reinforcement learning (RL), which operates solely on static datasets without further
interactions with the environment, provides an appealing alternative to learning a safe and …

Model-based reinforcement learning (RL), which learns environment model from offline
dataset and generates more out-of-distribution model data, has become an effective …

The foldable wave-energy powered autonomous underwater vehicle (FWEPAUV) is capable
of directly generating sufficient electrical energy from seawater when its body aligns …

To alleviate the extrapolation error and instability inherent in Q-function directly learned by
off-policy Q-learning (QL-style) on static datasets, this article utilizes the on-policy state …

In recent years, reinforcement learning (RL) has made great achievements in artificial
intelligence. Proximal policy optimization (PPO) is a representative RL algorithm, which …

Offline reinforcement learning (RL) learns policies for solving sequential decision problems
directly from offline datasets. Most existing works focus on countering out-of-distribution …