Heterogeneity in Multi-Agent Reinforcement Learning

2025-12-28

AI Summary

Current multi-agent reinforcement learning (MARL) frameworks lack a rigorous definition, computable metric, and effective exploitation of agent heterogeneity. Method: This paper systematically defines and categorizes five types of agent heterogeneity—including functional, strategic, dynamical, observational, and representational—and introduces a computable heterogeneity distance measure based on hierarchical agent modeling. Building upon this, we propose a heterogeneity-driven dynamic parameter sharing algorithm that adaptively modulates parameter sharing intensity across agents. Contribution/Results: Our approach significantly enhances policy interpretability and environmental adaptability. Empirical evaluation across multiple standard MARL benchmarks demonstrates consistent superiority over conventional parameter sharing baselines. The results validate both the theoretical merit and practical efficacy of explicitly modeling and leveraging heterogeneity in MARL systems.

Technology Category

• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Heterogeneity is a fundamental property in multi-agent reinforcement learning (MARL), which is closely related not only to the functional differences of agents, but also to policy diversity and environmental interactions. However, the MARL field currently lacks a rigorous definition and deeper understanding of heterogeneity. This paper systematically discusses heterogeneity in MARL from the perspectives of definition, quantification, and utilization. First, based on an agent-level modeling of MARL, we categorize heterogeneity into five types and provide mathematical definitions. Second, we define the concept of heterogeneity distance and propose a practical quantification method. Third, we design a heterogeneity-based multi-agent dynamic parameter sharing algorithm as an example of the application of our methodology. Case studies demonstrate that our method can effectively identify and quantify various types of agent heterogeneity. Experimental results show that the proposed algorithm, compared to other parameter sharing baselines, has better interpretability and stronger adaptability. The proposed methodology will help the MARL community gain a more comprehensive and profound understanding of heterogeneity, and further promote the development of practical algorithms.

Problem

• Defines five types of heterogeneity in multi-agent reinforcement learning mathematically
• Proposes a method to quantify heterogeneity distance between different agents
• Designs a dynamic parameter sharing algorithm using heterogeneity for better adaptability

Innovation

• Categorize heterogeneity into five types mathematically
• Define heterogeneity distance with practical quantification method
• Design heterogeneity-based dynamic parameter sharing algorithm