583 citations · 656 across the 6 of their papers we have counts for
6 papers
Efficient Anti-exploration via VQVAE and Fuzzy Clustering in Offline Reinforcement Learning
Long Chen, Yinkui Liu, Shen Li +2
Pseudo-count is an effective anti-exploration method in offline reinforcement learning (RL) by counting state-action pairs and imposing a large penalty on rare or unseen state-acti…
DiffPoGAN: Diffusion Policies with Generative Adversarial Networks for Offline Reinforcement Learning
Xuemin Hu, Shen Li, Yingfen Xu +2
Offline reinforcement learning (RL) can learn optimal policies from pre-collected offline datasets without interacting with the environment, but the sampled actions of the agent ca…
Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving
Xuemin Hu, Pan Chen, Yijun Wen +2
Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of in…
FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion
Siyu Teng, Luxi Li, Yuchen Li +4
In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limit…
How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence
Xuemin Hu, Shen Li, Tingyu Huang +3
Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehic…
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
Siyu Teng, Xuemin Hu, Peng Deng +8
Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial…