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

TL;DR

This paper introduces a comprehensive framework for evaluating and understanding adaptability in Multi-Agent Reinforcement Learning, emphasizing its importance for real-world applications and proposing structured assessment dimensions.

Contribution

It proposes a unified adaptability framework with three key dimensions, enhancing evaluation methods for MARL in dynamic environments.

Findings

Defines adaptability as a key performance measure for MARL

Introduces a structured framework with three adaptability dimensions

Aims to improve MARL deployment in real-world systems

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 \textit{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.