HLSMAC：面向高层战略决策的新型星际争霸多智能体挑战
HLSMAC: A New StarCraft Multi-Agent Challenge for High-Level Strategic Decision-Making
AI总结：
本文提出了HLSMAC基准，基于《三十六计》设计12个星际争霸2场景，以评估多智能体强化学习的高层战略决策能力，并引入超越胜率的新指标，验证了其作为稳健测试平台的有效性。
摘要：
基准测试对于评估多智能体强化学习（MARL）算法至关重要。虽然与星际争霸2相关的环境推动了MARL的重大进展，但像SMAC这样的现有基准主要侧重于微操，限制了对高层战略智能的全面评估。为了解决这个问题，我们引入了HLSMAC，这是一个新的合作式MARL基准，包含12个基于《三十六计》中经典计谋精心设计的星际争霸2场景。每个场景对应一个具体计谋，旨在通过战术机动、时机协调和欺骗等多样的战略元素来挑战智能体，从而为评估高层战略决策能力开辟了新途径。我们还提出了超越常规胜率的多维度新指标，例如能力利用率和推进效率，以评估智能体在HLSMAC环境中的整体表现。我们将最先进的MARL算法和基于LLM的智能体集成到我们的基准中，并进行了全面实验。结果表明，HLSMAC是推进多智能体战略决策的稳健测试平台。
英文摘要：
Benchmarks are crucial for assessing multi-agent reinforcement learning (MARL) algorithms. While StarCraft II-related environments have driven significant advances in MARL, existing benchmarks like SMAC focus primarily on micromanagement, limiting comprehensive evaluation of high-level strategic intelligence. To address this, we introduce HLSMAC, a new cooperative MARL benchmark with 12 carefully designed StarCraft II scenarios based on classical stratagems from the Thirty-Six Stratagems. Each scenario corresponds to a specific stratagem and is designed to challenge agents with diverse strategic elements, including tactical maneuvering, timing coordination, and deception, thereby opening up avenues for evaluating high-level strategic decision-making capabilities. We also propose novel metrics across multiple dimensions beyond conventional win rate, such as ability utilization and advancement efficiency, to assess agents' overall performance within the HLSMAC environment. We integrate state-of-the-art MARL algorithms and LLM-based agents with our benchmark and conduct comprehensive experiments. The results demonstrate that HLSMAC serves as a robust testbed for advancing multi-agent strategic decision-making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
