Qingxu Fu

Orcid: 0000-0002-5120-2046

According to our database1, Qingxu Fu authored at least 14 papers between 2020 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
SeeUPO: Sequence-Level Agentic-RL with Convergence Guarantees.
CoRR, February, 2026

Unreal-MAP: Unreal-Engine-Based General Platform for Multi-agent Reinforcement Learning.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
AgentEvolver: Towards Efficient Self-Evolving Agent System.
CoRR, November, 2025

Taming the Judge: Deconflicting AI Feedback for Stable Reinforcement Learning.
CoRR, October, 2025

A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning.
IEEE Trans. Neural Networks Learn. Syst., May, 2025

Self-Clustering Hierarchical Multi-Agent Reinforcement Learning With Extensible Cooperation Graph.
IEEE Trans. Emerg. Top. Comput. Intell., April, 2025

2024
Fuzzy Feedback Multiagent Reinforcement Learning for Adversarial Dynamic Multiteam Competitions.
IEEE Trans. Fuzzy Syst., May, 2024

Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems.
CoRR, 2024

2023
Learning Superior Cooperative Policy in Adversarial Multi-Team Reinforcement Learning.
Proceedings of the International Joint Conference on Neural Networks, 2023

2022
Learning Heterogeneous Agent Cooperation via Multiagent League Training.
CoRR, 2022

Solving the Diffusion of Responsibility Problem in Multiagent Reinforcement Learning with a Policy Resonance Approach.
CoRR, 2022

A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning.
Proceedings of the International Joint Conference on Neural Networks, 2022

Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2020
Learning an adaptive model for extreme low-light raw image processing.
IET Image Process., 2020


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