Collaboration Promotes Group Resilience in Multi-Agent RL
This addresses resilience for multi-agent systems, but it is incremental as it extends single-agent concepts to multi-agent settings.
The paper tackles the problem of resilience to environmental changes in multi-agent reinforcement learning by introducing group resilience, and finds that collaborative approaches achieve higher resilience than non-collaborative ones.
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.