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Jing Chen

National University of Defense Technology

Changsha Hunan, 410073

China

SCHOLARLY PAPERS

4

DOWNLOADS

112

TOTAL CITATIONS

1

Scholarly Papers (4)

1.

Finding and Using Stable Features: An Effective Way for Opponent Hidden Information Inference in Imperfect Information Games

Number of pages: 32 Posted: 01 Oct 2022
National University of Defense Technology, National University of Defense Technology, National University of Defense Technology, National University of Defense Technology and National University of Defense Technology
Downloads 52 (1,041,696)

Abstract:

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opponent hidden information inference, state estimation, stable feature, action model, Texas Hold'em

2.

Vaos: Enhancing the Stability of Cooperative Multi-Agent Policy Learning

Number of pages: 35 Posted: 11 Jun 2024
National University of Defense Technology, National University of Defense Technology, National University of Defense Technology, National University of Defense Technology and National University of Defense Technology
Downloads 34 (1,263,436)
Citation 1

Abstract:

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Overestimation reductionMulti-agent Operator switchingValue averagingReinforcement Learning.

3.

Risca: Enhancing the Interpretability of Cooperative Multi-Agent Policy Learning

Number of pages: 24 Posted: 26 May 2025
Peng Li, Shaofei Chen, Jing Chen and Zhenzhen Hu
National University of Defense Technology, National University of Defense Technology, National University of Defense Technology and National University of Defense Technology
Downloads 14 (1,509,872)

Abstract:

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Multi-agent InterpretabilityRisk-sensitive Reinforcement Learning Cooperative Policy.

4.

Distributional weighted multi-agent policy learning method with adaptive risk attitudes

Number of pages: 39 Posted: 27 Mar 2026
Peng Li, Zhenzhen Hu and Jing Chen
National University of Defense Technology, National University of Defense Technology and National University of Defense Technology
Downloads 12 (1,526,714)

Abstract:

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Multi-agent \sep Adaptive risk attitudes \sep Distributional \sep Reinforcement learning \sep Risk-sensitive.