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This is a Quantitative Study study.

September 20, 20255 citations

L2M2: Integrating Large Language Models with Multi-Agent Reinforcement Learning

L2M2: A Hierarchical Framework Integrating Large Language Model and Multi-agent Reinforcement Learning

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L2M2 demonstrates improved performance in multi-agent tasks with zero-shot planning and without subgoals.

L2M2's framework enables zero-shot planning, improving multi-agent task handling while reducing training sample usage.
In experiments, L2M2's LLM-guided MARL achieved superior performance with under 20% of baseline training samples.
Analysis indicates L2M2 can automatically generate navigation plans, enhancing effectiveness in complex scenarios.
The framework showcases robust abilities in both subgoal-defined tasks and those lacking predefined objectives.

Geng et al. (2025) studied this question.

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September 20, 20255 citations

L2M2: A Hierarchical Framework Integrating Large Language Model and Multi-agent Reinforcement Learning

View Full Paper

Ask AI

Bookmark

Authors

L2M2 demonstrates improved performance in multi-agent tasks with zero-shot planning and without subgoals.

L2M2's framework enables zero-shot planning, improving multi-agent task handling while reducing training sample usage.
In experiments, L2M2's LLM-guided MARL achieved superior performance with under 20% of baseline training samples.
Analysis indicates L2M2 can automatically generate navigation plans, enhancing effectiveness in complex scenarios.
The framework showcases robust abilities in both subgoal-defined tasks and those lacking predefined objectives.

Geng et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark