From the 1 of 8 linked papers with an AI index.
8 papers
RAD: Retrieval High-quality Demonstrations to Enhance Decision-making
Lu Guo, Yixiang Shan, Zhengbang Zhu +5
The paper proposes RAD, a method that improves offline reinforcement learning by retrieving high-return states from the dataset and generating sub-trajectories toward these targets…
DyDiff: Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning
Hanye Zhao, Xiaoshen Han, Zhengbang Zhu +4
With the great success of diffusion models (DMs) in generating realistic synthetic vision data, many researchers have investigated their potential in decision-making and control. M…
SocialDriveGen: Generating Diverse Traffic Scenarios with Controllable Social Interactions
Jiaguo Tian, Zhengbang Zhu, Shenyu Zhang +6
The generation of realistic and diverse traffic scenarios in simulation is essential for developing and evaluating autonomous driving systems. However, most simulation frameworks r…
DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models
Shenyu Zhang, Jiaguo Tian, Zhengbang Zhu +3
Microscopic traffic simulation has become an important tool for autonomous driving training and testing. Although recent data-driven approaches advance realistic behavior generatio…
Score-Based Diffusion Policy Compatible with Reinforcement Learning via Optimal Transport
Mingyang Sun, Pengxiang Ding, Weinan Zhang +1
Diffusion policies have shown promise in learning complex behaviors from demonstrations, particularly for tasks requiring precise control and long-term planning. However, they face…
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization
Shutong Ding, Ke Hu, Zhenhao Zhang +5
Diffusion models have garnered widespread attention in Reinforcement Learning (RL) for their powerful expressiveness and multimodality. It has been verified that utilizing diffusio…