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Yuhang Jiang

14 papers hereh-index 10269 citations19 works total

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

author position
  • first author2
  • middle author11

Across the 13 of 14 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.AI4
  • cs.CV2
  • cs.CL1
same name
  • Yuhang Jiang — 16 papers, h 18
  • Yuhang Jiang — 7 papers, h 4
  • Yuhang Jiang — 6 papers, h 2
  • Yuhang Jiang — 4 papers, h 4
  • Yuhang Jiang — 4 papers, h 2
  • Yuhang Jiang — 1 paper, 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
20212026
most citedCredit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning

5 citations · 14 across the 12 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.LG2024

Doubly Mild Generalization for Offline Reinforcement Learning

Yixiu Mao, Qi Wang, Yun Qu +2

Offline Reinforcement Learning (RL) suffers from the extrapolation error and value overestimation. From a generalization perspective, this issue can be attributed to the over-gener…

cs.AI2024

Stop Wandering, Find the Keys: LLMs Discriminate Key States for Efficient Multi-Agent Exploration

Yun Qu, Boyuan Wang, Yuhang Jiang +7

With expansive state-action spaces, efficient multi-agent exploration remains a longstanding challenge in reinforcement learning. Although pursuing novelty, diversity, or uncertain…

cs.AI2024★ 1 cited

Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks

Yun Qu, Boyuan Wang, Jianzhun Shao +15

The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre-collected o…

cs.AI2024★ 1 cited

LLM-Empowered State Representation for Reinforcement Learning

Boyuan Wang, Yun Qu, Yuhang Jiang +4

Conventional state representations in reinforcement learning often omit critical task-related details, presenting a significant challenge for value networks in establishing accurat…

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