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arXiv · 2503.05383

AVA: Attentive VLM Agent for Mastering StarCraft II

Weiyu Ma·Yuqian Fu·Zecheng Zhang·Bernard Ghanem·Guohao Li
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Abstract

We introduce AVACraft, a multimodal StarCraft II benchmark supporting both Multi-Agent Reinforcement Learning (MARL) and Vision-Language Model (VLM) paradigms. Unlike SMAC-family environments that rely on abstract state representations and exclude VLMs, AVACraft provides RGB visuals, natural language observations, and structured state information, enabling systematic comparison between training-based and zero-shot methods across 21 scenarios spanning micromanagement, coordination, and strategic planning. We establish comprehensive baselines: six MARL algorithms (IQL, QMIX, QTRAN, VDN, MAPPO, IPPO) with Swin-Transformer backbones trained for 5M steps, and multiple VLMs including proprietary (GPT-4o) and open-source (Qwen3-VL) models. Results reveal complementary strengths-MARL peaks at 19.3% win rate after 5M steps, while VLMs achieve 75-90% zero-shot with human-aligned decisions-exposing trade-offs between training efficiency, performance ceilings, interpretability, and deployment cost. Code: https://github.com/camel-ai/VLM-Play-StarCraft2.

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Weiyu Ma, Yuqian Fu, Zecheng Zhang, Bernard Ghanem, Guohao Li. 2025-03-07. AVA: Attentive VLM Agent for Mastering StarCraft II. https://arxiv.org/abs/2503.05383

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