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MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation

arXiv 2607.27967 ↗First posted 30 Jul 2026cs.AIcited by 0

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Abstract (arXiv)

Credit assignment is a fundamental challenge in cooperative multi-agent reinforcement learning, particularly in embodied AI settings characterized by limited and delayed feedback as well as dynamically changing numbers of active agents. We propose MARS-RA, a framework that reformulates credit assignment as a rank aggregation problem using contribution-based pairwise comparisons among agents generated by large multimodal models. This shift from absolute to relative estimation ensures robustness against noise and dynamic agent participation, converting comparison results into contribution scores for potential-based reward shaping. We provide theoretical justification for the convergence and robustness of the proposed framework, and show that Shapley values can be used as an interpretive reference. Experimental results on challenging tasks of different types indicate that MARS-RA can guide agents toward effective cooperation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 63 references that are in the map. First posted on arXiv 30 Jul 2026.

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References · 23

  1. 2017Deep reinforcement learning from human preferencescited by 3,846
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11 more
  1. 2015Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependencecited by 59
  2. 2024COMBO: Compositional World Models for Embodied Multi-Agent Cooperationcited by 47
  3. 2025Interleaved Latent Visual Reasoning with Selective Perceptual Modelingcited by 30
  4. 2021SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learningcited by 26
  5. 2025Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignmentcited by 17
  6. 2022A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Gamecited by 16
  7. 2026VideoThinker: Building Agentic VideoLLMs with LLM-Guided Tool Reasoningcited by 7
  8. 2023XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Librarycited by 5
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  10. 2026TowerMind: A Tower Defence Game Learning Environment and Benchmark for LLM as Agentscited by 4
  11. 2025Challenges in Credit Assignment for Multi-Agent Reinforcement Learning in Open Agent Systemscited by 2

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  4. 2025Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agents
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  8. 2026Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning