10 citations · 11 across the 8 of their papers we have counts for
4 papers · 1 filter
Adaptive Command: Real-Time Policy Adjustment via Language Models in StarCraft II
Weiyu Ma, Dongyu Xu, Shu Lin +2
We present Adaptive Command, a novel framework integrating large language models (LLMs) with behavior trees for real-time strategic decision-making in StarCraft II. Our system focu…
EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
Yang Cheng, Weiyu Ma, Zilai Wang +6
Complex decision-making often requires agents to progress through intermediate tasks rather than solve the final target directly. Existing LLM self-refinement methods typically ite…
TacticCraft: Natural Language-Driven Tactical Adaptation for StarCraft II
Weiyu Ma, Jiwen Jiang, Haobo Fu +1
We present an adapter-based approach for tactical conditioning of StarCraft II AI agents. Current agents, while powerful, lack the ability to adapt their strategies based on high-l…
Evolving LLMs' Self-Refinement Capability via Synergistic Training-Inference Optimization
Yongcheng Zeng, Xinyu Cui, Xuanfa Jin +11
Self-Refinement refers to a model's ability to revise its own responses to produce improved outputs. This capability can also serve as a fundamental mechanism for Self-Improvement,…