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Huazhe Xu

29 papers hereh-index 233.4k citations39 works total

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

author position
  • first author2
  • middle author16
  • last author9

Across the 27 of 29 papers where every author was matched, so the position is known.

fields
  • cs.LG16
  • cs.AI6
  • cs.CV6
  • cs.RO1
same name
  • Huazhe Xu — 20 papers, h 14
  • Huazhe Xu — 16 papers, h 14
  • Huazhe Xu — 12 papers, h 8
  • Huazhe Xu — 9 papers, h 5
  • Huazhe Xu — 7 papers, h 6
  • Huazhe Xu — 6 papers, h 2

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
20162023
most citedMulti-Person 3D Motion Prediction with Multi-Range Transformers

35 citations · 133 across the 19 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.LG2022★ 13 cited

Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning

Zhecheng Yuan, Zhengrong Xue, Bo Yuan +4

Learning generalizable policies that can adapt to unseen environments remains challenging in visual Reinforcement Learning (RL). Existing approaches try to acquire a robust represe…

cs.AI2022★ 2 cited

E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance

Can Chang, Ni Mu, Jiajun Wu +2

A critical challenge in multi-agent reinforcement learning(MARL) is for multiple agents to efficiently accomplish complex, long-horizon tasks. The agents often have difficulties in…

cs.LG2022★ 1 cited

Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation

Linfeng Zhao, Huazhe Xu, Lawson L. S. Wong

Differentiable planning promises end-to-end differentiability and adaptivity. However, an issue prevents it from scaling up to larger-scale problems: they need to differentiate thr…

cs.AI2022★ 3 cited

Simple Emergent Action Representations from Multi-Task Policy Training

Pu Hua, Yubei Chen, Huazhe Xu

The low-level sensory and motor signals in deep reinforcement learning, which exist in high-dimensional spaces such as image observations or motor torques, are inherently challengi…

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