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arXiv · 2509.06235

PillagerBench: Benchmarking LLM-Based Agents in Competitive Minecraft Team Environments

Olivier Schipper·Yudi Zhang·Yali Du·Mykola Pechenizkiy·Meng Fang
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Abstract

LLM-based agents have shown promise in various cooperative and strategic reasoning tasks, but their effectiveness in competitive multi-agent environments remains underexplored. To address this gap, we introduce PillagerBench, a novel framework for evaluating multi-agent systems in real-time competitive team-vs-team scenarios in Minecraft. It provides an extensible API, multi-round testing, and rule-based built-in opponents for fair, reproducible comparisons. We also propose TactiCrafter, an LLM-based multi-agent system that facilitates teamwork through human-readable tactics, learns causal dependencies, and adapts to opponent strategies. Our evaluation demonstrates that TactiCrafter outperforms baseline approaches and showcases adaptive learning through self-play. Additionally, we analyze its learning process and strategic evolution over multiple game episodes. To encourage further research, we have open-sourced PillagerBench, fostering advancements in multi-agent AI for competitive environments.

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Olivier Schipper, Yudi Zhang, Yali Du, Mykola Pechenizkiy, Meng Fang. 2025-09-07. PillagerBench: Benchmarking LLM-Based Agents in Competitive Minecraft Team Environments. https://doi.org/10.1109/cog64752.2025.11114387

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