9 citations · 16 across the 4 of their papers we have counts for
7 papers
Safe Driving via Expert Guided Policy Optimization
Zhenghao Peng, Quanyi Li, Chunxiao Liu +1
When learning common skills like driving, beginners usually have domain experts standing by to ensure the safety of the learning process. We formulate such learning scheme under th…
Autonomous Underwater Vehicle-Manipulator Systems Path Planning with RRTAUVMS Algorithm
Xiaoxu Cao, Linyi Gu, JunChen Mu +4
Autonomous Underwater Vehicle-Manipulator systems (AUVMS) is a new tool for ocean exploration, the AUVMS path planning problem is addressed in this paper. AUVMS is a high dimension…
Improving the Generalization of End-to-End Driving through Procedural Generation
Quanyi Li, Zhenghao Peng, Qihang Zhang +2
Over the past few years there is a growing interest in the learning-based self driving system. To ensure safety, such systems are first developed and validated in simulators before…
Understanding the wiring evolution in differentiable neural architecture search
Sirui Xie, Shoukang Hu, Xinjiang Wang +4
Controversy exists on whether differentiable neural architecture search methods discover wiring topology effectively. To understand how wiring topology evolves, we study the underl…
DSNAS: Direct Neural Architecture Search without Parameter Retraining
Shoukang Hu, Sirui Xie, Hehui Zheng +4
If NAS methods are solutions, what is the problem? Most existing NAS methods require two-stage parameter optimization. However, performance of the same architecture in the two stag…
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
Sirui Xie, Junning Huang, Lanxin Lei +4
Reinforcement learning agents need exploratory behaviors to escape from local optima. These behaviors may include both immediate dithering perturbation and temporally consistent ex…