Author

Yan Zheng

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Sep 2026

LLM-guided contrastive role representation for diverse multi-agent coordination

Experiments on the StarCraft Multi-Agent Challenge benchmark demonstrate that CLRE achieves superior win rates and sample efficiency compared with state-of-the-art MARL methods, showing that the proposed approach enables diverse and efficient cooperation among agents in complex environments.

Tingting Wei, Yan Zheng, Zhang-Ling Wang et al. · Autonomous Agents and Multi-... · 0 citations

#machine learning Conference Open access Jun 2023

T3S: Improving Multi-Task Reinforcement Learning with Task-Specific Feature Selector and Scheduler

A novel MTRL framework called Task-Specific feature Selector and Scheduler (T3S) is proposed, which consists of two components: a feature selector and a task scheduler that consistently outperforms the state-of-the-art M TRL algorithms on various robotics manipulation tasks.

Yuan-Qiang Yu, Tianpei Yang, Yongliang Lv et al. · IEEE International Joint Con... · 4 citations

2025

LaRes: Evolutionary Reinforcement Learning with LLM-based Adaptive Reward Search

This work proposes LaRes, a novel hybrid framework that achieves efficient policy learning through reward function search by leveraging large language models to generate the reward function population, guiding RL in policy learning.

Pengyi Li, Hongyao Tang, Jinbin Qiao et al. · Neural Information Processin... · 7 citations