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researcher

Hua Wei

Arizona State University

22 papers hereh-index 204.7k citations53 works total

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

author position
  • first author6
  • middle author8
  • last author6

Across the 20 of 22 papers where every author was matched, so the position is known.

fields
  • cs.LG16
  • cs.AI3
  • cs.MA1
  • cs.SI1
  • eess.SY1
affiliations
  • Arizona State University
Homepage
same name
  • Hua Wei — 27 papers, h 8
  • Hua Wei — 8 papers, h 3
  • Hua Wei — 8 papers, h 3
  • Hua Wei — 6 papers, h 7
  • Hua Wei — 6 papers, h 4
  • Hua Wei — 4 papers, h 3

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 citedCoLight: Learning Network-level Cooperation for Traffic Signal Control

445 citations · 888 across the 21 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.LG2022★ 8 cited

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

Wenlu Du, Junyi Ye, Jingyi Gu +3

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the…

cs.LG2022★ 45 cited

LibSignal: An Open Library for Traffic Signal Control

Hao Mei, Xiaoliang Lei, Longchao Da +2

This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state…

cs.LG2022★ 12 cited

Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning

Hua Wei, Jingxiao Chen, Xiyang Ji +11

This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on Honor of Kings, one of the world's most popular games at present. Compared to other e…

cs.LG2022★ 23 cited

Modeling Network-level Traffic Flow Transitions on Sparse Data

Xiaoliang Lei, Hao Mei, Bin Shi +1

Modeling how network-level traffic flow changes in the urban environment is useful for decision-making in transportation, public safety and urban planning. The traffic flow system…

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