SMAC-Talk is a new benchmark that adds natural language messaging and deceptive-agent scenarios to SMAC for testing LLM coordination in multi-agent environments.
Large language models play starcraft ii: Benchmarks and a chain of summarization approach.arXiv preprint arXiv:2312.11865,
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
COMPASS uses VLMs to generate and refine code-based strategies with structured communication, achieving 57% win rate on SMACv2 Protoss 5v5 versus 27% for QMIX.
Prompt-conditioned LLMs face irreducible error floors from language's limited information capacity and alignment constraints, proven via PAC-Bayes bounds on bilevel cheap-talk games for certain task families.
ROE framework lets LLM defeat Very Hard bot in TextStarCraft II via keyframe selection, expert/self-experience decisions, and post-game reflection for new self-experience.
citing papers explorer
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SMAC-Talk: A Natural Language Extension of the StarCraft Multi-Agent Challenge for Large Language Models
SMAC-Talk is a new benchmark that adds natural language messaging and deceptive-agent scenarios to SMAC for testing LLM coordination in multi-agent environments.
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Closed-Loop Vision-Language Planning for Multi-Agent Coordination
COMPASS uses VLMs to generate and refine code-based strategies with structured communication, achieving 57% win rate on SMACv2 Protoss 5v5 versus 27% for QMIX.
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On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners
Prompt-conditioned LLMs face irreducible error floors from language's limited information capacity and alignment constraints, proven via PAC-Bayes bounds on bilevel cheap-talk games for certain task families.
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Reflection of Episodes: Learning to Play Game from Expert and Self Experiences
ROE framework lets LLM defeat Very Hard bot in TextStarCraft II via keyframe selection, expert/self-experience decisions, and post-game reflection for new self-experience.