# Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment

**Authors:** Kartik Nagpal, Dayi Dong, Jean-Baptiste Bouvier, Negar Mehr  
**arXiv:** [2502.16863](https://arxiv.org/abs/2502.16863)  
**Date:** 2025-02-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2502.16863v1/

## Key points

- Authors Kartik Nagpal, Dayi Dong, Jean-Baptiste Bouvier, and Negar Mehr address evaluating individual agents' contributions in a team towards shared goals
- Importance of learning collaborative behavior in domains like autonomous vehicle coordination and in-space assembly emphasized
- Proposal of novel approach LLM-MCA (Large Language Model-Multi-Agent Credit Assignment) utilizing Large Language Models to solve credit assignment problem
- Reformulation of credit assignment as sequence improvement and attribution pattern recognition problems using centralized LLM reward-critic
- Feedback from LLM used to update policy networks of agents for personalized evaluations and performance improvement
- Introduction of extension LLM-TACA where LLM critic assigns tasks by providing intermediary goals directly to each agent's policy
- Outperformance of existing methods on benchmarks including Level-Based Foraging, Robotic Warehouse tasks, and Spaceworld benchmark with collision-related safety constraints
- Generation of large trajectory datasets with per-agent reward information sampled from LLM critics due to proposed methods
- Advancement in Multi-Agent Reinforcement Learning demonstrated through integration of human-level performance from Large Language Models for enhancing collaborative behavior learning in robotic systems

## Summary

In their paper titled "Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment," authors Kartik Nagpal, Dayi Dong, Jean-Baptiste Bouvier, and Negar Mehr address the challenge of evaluating the contributions of individual agents in a team towards achieving shared goals. They emphasize the importance of learning collaborative behavior in various domains such as autonomous vehicle coordination and in-space assembly. The authors propose a novel approach called LLM-MCA (Large Language Model-Multi-Agent Credit Assignment) that utilizes Large Language Models (LLMs) to solve the credit assignment problem. This method reformulates credit assignment as two pattern recognition problems: sequence improvement and attribution. By using a centralized LLM reward-critic, it decomposes the environment reward based on each agent's contribution within a scenario. This feedback is then used to update the policy networks of the agents, enabling them to improve their performance through personalized evaluations. Additionally, the authors introduce an extension called LLM-TACA where the LLM critic explicitly assigns tasks by providing intermediary goals directly to each agent's policy. Both LLM-MCA and LLM-TACA outperform existing methods on various benchmarks including Level-Based Foraging, Robotic Warehouse tasks, and a new Spaceworld benchmark that incorporates collision-related safety constraints. As a result of their methods, large trajectory datasets are generated with detailed per-agent reward information at each timestep sampled from the LLM critics. The authors' work not only advances Multi-Agent Reinforcement Learning but also demonstrates how integrating human-level performance from Large Language Models can enhance collaborative behavior learning in robotic systems.