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A Smooth Reinforcement Learning Method for Trajectory Tracking and Collision Avoidance of Wheeled Vehicle

We proposed a smooth policy network (LipsNeXt) and a smooth distributed soft actor-critic (DSAC-S) algorithm to achieve coordinated optimization of control precision and action smoothness.

Liangfa Chen*, Xujie Song*, Wenxuan Wang, Yinuo Wang, Liming Xiao, Junjie Zhao, Shengbo Eben Li, Fei Ma, Jingliang Duan

A Smooth Reinforcement Learning Method for Trajectory Tracking and Collision Avoidance of Wheeled Vehicle

Smooth Reinforcement Learning Based Trajectory Tracking for Articulated Vehicles

This study addresses the problem of action fluctuation in the trajectory tracking control of articulated vehicles, proposing a smooth …

Liangfa Chen*, Xujie Song*, Liming Xiao, Lulu Gao, Fawang Zhang, Shengbo Eben Li, Fei Ma, Jingliang Duan

Smooth Reinforcement Learning Based Trajectory Tracking for Articulated Vehicles

Training Multilayer Neural Networks on Ising Machines

We proposed Ising learning algorithm, the first technique to train multilayer feedforward neural networks on Ising machines (quantum computers). The training time is reduced by 90% compared to CPU/GPU.

Xujie Song, Tong Liu, Jingliang Duan, Wenxuan Wang, Yin Ma, Hai Wei, Kai Wen, Shengbo Eben Li

Training Multilayer Neural Networks on Ising Machines

LiDAR Data Enhancement via Pseudo-LiDAR Point Cloud Correction

Objective Light detection and ranging (LiDAR) plays an important role in autonomous driving. Even if it is expensive, the number of …

Xujie Song, Sunhao Dai, Chunyu Lin, Shutao Zhan, Yao Zhao

LiDAR Data Enhancement via Pseudo-LiDAR Point Cloud Correction

Smooth Filtering Neural Network for Reinforcement Learning

We proposed a policy network for RL with low-pass filtering ability, named Smonet, to alleviate the action nonsmoothness issue by learning a low-frequency representation within hidden layers.

Wenxuan Wang, Jingliang Duan, Xujie Song, Liming Xiao, Liangfa Chen, Yinuo Wang, Bo Cheng, Shengbo Eben Li

Smooth Filtering Neural Network for Reinforcement Learning