Reproduce “Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment” on OpenResearch

About this paper

Kartik Nagpal, Dayi Dong, Jean-Baptiste Bouvier, Negar Mehr

Nagpal et al. introduce LLM-MCA and LLM-TACA, methods that leverage large language models as a centralized critic to address the multi-agent structural credit assignment problem in cooperative reinforcement learning. These approaches yield superior performance across various benchmarks, including a new safety-critical Spaceworld environment, while providing interpretable natural language explanations for credit and task assignments.

Key insights

• Large Language Models possess advanced pattern recognition and complex reasoning capabilities that can be effectively leveraged to emulate human-like, nuanced credit evaluation in multi-agent systems.
• Framing the core challenge as an "Agreement Problem" and explicitly guiding the LLM to address under- and over-collaboration leads to more effective and less myopic learning signals for agents.
• Integrating explicit task assignments via LLM-TACA during centralized training, while maintaining decentralized execution through controlled input dropout, further enhances agent coordination and overall system performance.

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