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The aim is to enhance decision-making in multi-agent reinforcement learning by integrating high-level guidance from large language models.

May 27, 2026Scientific ReportsOpen Access

A hierarchical multi-agent reinforcement learning framework with high-level guidance from large language models

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Randomized trial demonstrates improved learning efficiency in multi-agent systems using hierarchical LLM guidance, suggesting a promising framework.

The aim is to enhance decision-making in multi-agent reinforcement learning by integrating high-level guidance from large language models.
Proposed the LEHCA framework that incorporates a large language model as a Commander for strategic guidance.
Used QMIX-based low-level agents for action-level decision-making with high-level semantic inputs.
Conducted experiments in the StarCraft multi-agent challenge across eight scenarios and implemented ablation studies.
LEHCA outperforms QMIX in various metrics on heterogeneous and sparse-reward scenarios.
Demonstrated stronger early-stage learning efficiency compared to QPLEX, MAVEN, and MAPPO.
Additional experiments indicate the framework's applicability beyond StarCraft, highlighting its versatility.

Bai et al. (2026) studied this question.

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May 27, 2026Scientific ReportsOpen Access

View Full Paper

Ask AI

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Authors

Randomized trial demonstrates improved learning efficiency in multi-agent systems using hierarchical LLM guidance, suggesting a promising framework.

The aim is to enhance decision-making in multi-agent reinforcement learning by integrating high-level guidance from large language models.
Proposed the LEHCA framework that incorporates a large language model as a Commander for strategic guidance.
Used QMIX-based low-level agents for action-level decision-making with high-level semantic inputs.
Conducted experiments in the StarCraft multi-agent challenge across eight scenarios and implemented ablation studies.
LEHCA outperforms QMIX in various metrics on heterogeneous and sparse-reward scenarios.
Demonstrated stronger early-stage learning efficiency compared to QPLEX, MAVEN, and MAPPO.
Additional experiments indicate the framework's applicability beyond StarCraft, highlighting its versatility.

Bai et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark