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Qiang Fu

21 papers hereh-index 191.5k citations38 works total

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

author position
  • middle author21

Across the 21 of 21 papers where every author was matched, so the position is known.

fields
  • cs.LG9
  • cs.AI7
  • cs.MA2
  • cs.CV1
  • cs.GT1
  • cs.NE1
same name
  • Qiang Fu — 21 papers, h 13
  • Qiang Fu — 12 papers, h 25
  • Qiang Fu — 12 papers, h 8
  • Qiang Fu — 10 papers, h 8
  • Qiang Fu — 7 papers, h 15
  • Qiang Fu — 7 papers, h 5

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
20182023
most citedSupervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

55 citations · 171 across the 21 of their papers we have counts for

collaborators
Showing 2020Show all

3 papers · 1 filter

cs.AI2020★ 42 cited

Towards Playing Full MOBA Games with Deep Reinforcement Learning

Deheng Ye, Guibin Chen, Wen Zhang +15

MOBA games, e.g., Honor of Kings, League of Legends, and Dota 2, pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc.…

cs.AI2020★ 4 cited

Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search

Sheng Chen, Menghui Zhu, Deheng Ye +3

Hero drafting is essential in MOBA game playing as it builds the team of each side and directly affects the match outcome. State-of-the-art drafting methods fail to consider: 1) dr…

cs.AI2020★ 55 cited

Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

Deheng Ye, Guibin Chen, Peilin Zhao +15

We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA…

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