Collaboration Promotes Group Resilience in Multi-Agent RL

TL;DR

This paper introduces the concept of group resilience in multi-agent reinforcement learning, demonstrating that collaboration among agents significantly enhances their ability to adapt to environmental changes.

Contribution

It formalizes group resilience in MARL and empirically shows that collaboration protocols improve agents' adaptability to environmental perturbations.

Findings

Collaborative MARL agents exhibit higher resilience than non-collaborative ones.

All examined collaboration protocols improve group resilience.

Empirical evidence supports the hypothesis that collaboration enhances adaptability.

Abstract

To effectively operate in various dynamic scenarios, RL agents must be resilient to unexpected changes in their environment. Previous work on this form of resilience has focused on single-agent settings. In this work, we introduce and formalize a multi-agent variant of resilience, which we term group resilience. We further hypothesize that collaboration with other agents is key to achieving group resilience; collaborating agents adapt better to environmental perturbations in multi-agent reinforcement learning (MARL) settings. We test our hypothesis empirically by evaluating different collaboration protocols and examining their effect on group resilience. Our experiments show that all the examined collaborative approaches achieve higher group resilience than their non-collaborative counterparts.