Problem
Research questions and friction points this paper is trying to address.
credit assignment
multi-agent reinforcement learning
embodied AI
dynamic agent participation
limited feedback
MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation
🤖 AI Summary
This work addresses the credit assignment challenge in embodied multi-agent collaboration, where sparse and delayed feedback, coupled with dynamically varying numbers of agents, hinders effective learning. To tackle this, the authors propose a novel credit assignment method integrating multimodal large language models with rank aggregation. The approach leverages a multimodal large model to generate pairwise relative comparisons of agent contributions, which are then converted into contribution scores via a rank aggregation algorithm. These scores are incorporated into potential-based reward shaping to guide policy optimization. By introducing rank aggregation and multimodal large models into credit assignment for the first time, the proposed relative comparison mechanism significantly enhances robustness to noise and dynamic participation while offering interpretability through alignment with Shapley values. Empirical results demonstrate consistent improvements in system performance and stability across diverse complex cooperative tasks.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Rank Aggregation
Credit Assignment
Multimodal Models
Multi-Agent Reinforcement Learning
Reward Shaping
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