Visual reinforcement learning (RL), which makes decisions directly from high-dimensional
visual inputs, has demonstrated significant potential in various domains. However …

Recent years have witnessed great potential in applying Deep Reinforcement Learning
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …

Plasticity, the ability of a neural network to evolve with new data, is crucial for high-
performance and sample-efficient visual reinforcement learning (VRL). Although methods …

Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual
reinforcement learning (RL) algorithms. Notably, employing simple observation …

This study focuses on the motion planning and risk evaluation of unprotected left turns at
occluded intersections for autonomous vehicles. In this paper, we present an interactive …

This study focuses on trajectory planning and motion control policies in autonomous racing,
which necessitates pushing the capacity boundaries of racing vehicles to achieve maximum …

Although deep deterministic policy gradient (DDPG) algorithm gets widespread attention as
a result of its powerful functionality and applicability for large-scale continuous control, it …

Offline reinforcement learning (RL) aims to learn an effective policy from a pre-collected
dataset. Most existing works are to develop sophisticated learning algorithms, with less …

Reinforcement Learning (RL) has become a promising technique to deal with the tough
decision modeling problem in the wargaming field. However, to deploy current RL …

Efficient motion and appearance modeling are critical for vision-based Reinforcement
Learning (RL). However, existing methods struggle to reconcile motion and appearance …