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researcher

H. Su

17 papers hereh-index 141.1k citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author7
  • last author9

Across the 17 of 17 papers where every author was matched, so the position is known.

fields
  • cs.LG14
  • cs.RO2
  • cs.CV1
same name
  • H. Su — 38 papers, h 16
  • H. Su — 21 papers, h 12
  • H. Su — 19 papers, h 0
  • H. Su — 17 papers, h 46
  • H. Su — 12 papers, h 7
  • H. Su — 10 papers, h 13

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedStabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation

37 citations · 138 across the 17 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.LG2023

Reparameterized Policy Learning for Multimodal Trajectory Optimization

Zhiao Huang, Litian Liang, Zhan Ling +3

We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy…

cs.RO2023★ 6 cited

On the Efficacy of 3D Point Cloud Reinforcement Learning

Zhan Ling, Yunchao Yao, Xuanlin Li +1

Recent studies on visual reinforcement learning (visual RL) have explored the use of 3D visual representations. However, none of these work has systematically compared the efficacy…

cs.LG2023★ 2 cited

Chain-of-Thought Predictive Control

Zhiwei Jia, Vineet Thumuluri, Fangchen Liu +3

We study generalizable policy learning from demonstrations for complex low-level control (e.g., contact-rich object manipulations). We propose a novel hierarchical imitation learni…

cs.LG2023★ 1 cited

Boosting Reinforcement Learning and Planning with Demonstrations: A Survey

Tongzhou Mu, Hao Su

Although reinforcement learning has seen tremendous success recently, this kind of trial-and-error learning can be impractical or inefficient in complex environments. The use of de…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.