Collaboration Promotes Group Resilience in Multi-Agent RL

Ilai Shraga, Guy Azran, Matthias Gerstgrasser, Ofir Abu, Jeffrey Rosenschein, Sarah Keren

Keywords: Multi-Agent Reinforcement Learning, Group Resilience, Collaboration, Deep Reinforcement Learning

TL;DR: We show that collaboration with other agents is key to achieving group resilience, meaning that collaborating agents adapt better to environment perturbations in multi-agent reinforcement learning (MARL) settings.

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.

Submission Number: 20