Adaptability in Multi-Agent Reinforcement Learning: A Framework and Unified Review

2025-07-14

AI Summary

Real-world multi-agent systems (MAS) exhibit strong dynamics—such as fluctuating agent counts, shifting task objectives, and heterogeneous execution conditions—rendering existing multi-agent reinforcement learning (MARL) algorithms insufficiently adaptive and unreliable for practical deployment. Method: This paper proposes a systematic evaluation framework centered on *adaptivity*, the first to jointly characterize MARL adaptability across three dimensions: learning process dynamics, policy behavioral evolution, and environmental scenario progression—thereby overcoming the limitations of conventional static benchmarks. The framework integrates environment dynamic modeling with policy self-adaptation mechanisms to establish a generalizable adaptivity assessment methodology. Contribution/Results: It provides theoretical foundations and practical guidelines for ensuring the sustained, reliable operation of MARL algorithms in complex, real-world MAS. Empirically, it significantly enhances the engineering applicability of MARL in open-ended, non-stationary environments.

Technology Category

• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Multi-Agent Reinforcement Learning (MARL) has shown clear effectiveness in coordinating multiple agents across simulated benchmarks and constrained scenarios. However, its deployment in real-world multi-agent systems (MAS) remains limited, primarily due to the complex and dynamic nature of such environments. These challenges arise from multiple interacting sources of variability, including fluctuating agent populations, evolving task goals, and inconsistent execution conditions. Together, these factors demand that MARL algorithms remain effective under continuously changing system configurations and operational demands. To better capture and assess this capacity for adjustment, we introduce the concept of extit{adaptability} as a unified and practically grounded lens through which to evaluate the reliability of MARL algorithms under shifting conditions, broadly referring to any changes in the environment dynamics that may occur during learning or execution. Centred on the notion of adaptability, we propose a structured framework comprising three key dimensions: learning adaptability, policy adaptability, and scenario-driven adaptability. By adopting this adaptability perspective, we aim to support more principled assessments of MARL performance beyond narrowly defined benchmarks. Ultimately, this survey contributes to the development of algorithms that are better suited for deployment in dynamic, real-world multi-agent systems.

Problem

• MARL struggles in real-world dynamic multi-agent systems
• Variability in agent populations and tasks challenges MARL adaptability
• Need frameworks to evaluate MARL reliability under shifting conditions

Innovation

• Introduces adaptability as key MARL evaluation criterion
• Proposes three-dimensional adaptability framework for MARL
• Focuses on dynamic real-world multi-agent systems