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Jiayu Chen

11 papers hereh-index 568 citations15 works total

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

author position
  • first author4
  • middle author7

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.RO7
  • cs.LG4
same name
  • Jiayu Chen — 23 papers, h 4
  • Jiayu Chen — 13 papers, h 2
  • Jiayu Chen — 11 papers, h 3
  • Jiayu Chen — 10 papers, h 14
  • Jiayu Chen — 8 papers, h 4
  • Jiayu Chen — 6 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
20232026
most citedOnline Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning

19 citations · 19 across the 11 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.LG2023

A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments

Jiayu Chen, Guosheng Li, Chao Yu +4

This paper addresses multi-UAV pursuit-evasion, where a group of drones cooperates to capture a fast evader in a confined environment with obstacles. Existing heuristic algorithms,…

cs.LG2023

MASP: Scalable GNN-based Planning for Multi-Agent Navigation

Xinyi Yang, Xinting Yang, Chao Yu +4

We investigate multi-agent navigation tasks, where multiple agents need to reach initially unassigned goals in a limited time. Classical planning-based methods suffer from expensiv…

cs.RO2023

Active Neural Topological Mapping for Multi-Agent Exploration

Xinyi Yang, Yuxiang Yang, Chao Yu +5

This paper investigates the multi-agent cooperative exploration problem, which requires multiple agents to explore an unseen environment via sensory signals in a limited time. A po…

cs.LG2023

Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning

Jiayu Chen, Zelai Xu, Yunfei Li +6

Learning Nash equilibrium (NE) in complex zero-sum games with multi-agent reinforcement learning (MARL) can be extremely computationally expensive. Curriculum learning is an effect…

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