A growing body of research has focused on the Reinforcement Learning (RL) methods
which allow the agent to learn from trial-and-error experiences gathered during the …

Reinforcement learning (RL) makes an agent learn from trial-and-error experiences
gathered during the interaction with the environment. Recently, offline RL has become a …

Offline reinforcement learning (RL) addresses the challenge of expensive and high-risk data
exploration inherent in RL by pre-training policies on vast amounts of offline data, enabling …

We study the problem of reward poisoning attacks against general offline reinforcement
learning with deep neural networks for function approximation. We consider a black-box …

Offline-to-online reinforcement learning has recently been shown effective in reducing the
online sample complexity by first training from offline collected data. However, this additional …

As reinforcement learning (RL) has achieved near human-level performance in a variety of
tasks, its robustness has raised great attention. While a vast body of research has explored …

Currently, reinforcement learning (RL), especially deep RL, has received more and more
attention in the research area. However, the security of RL has been an obvious problem …

Offline reinforcement learning (RL) faces a significant challenge of distribution shift. Model-
free offline RL penalizes the Q value for out-of-distribution (OOD) data or constrains the …

Reinforcement learning (RL) has achieved tremendous success in many complex decision
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …

Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-
critical real-world applications, such as autonomous driving, human-robot interaction, robot …