🤖 AI Summary
This study addresses collective resilience—the capacity of multi-agent systems (MAS) to maintain functionality and recover from sudden environmental disturbances in dynamic settings. Method: We formally define “multi-agent collective resilience,” departing from prior single-agent resilience paradigms, and propose and empirically validate collaboration as a core resilience-enhancing mechanism. Within a multi-agent reinforcement learning (MARL) framework, we design three collaborative protocols: shared observation, joint action prediction, and implicit coordination. Their resilience performance is systematically evaluated under injected environmental perturbations. Results: All protocols significantly improve task success rates and recovery speed; average resilience metrics increase by 37%–62% over non-collaborative baselines. These findings rigorously substantiate the central thesis that collaboration intrinsically enhances collective resilience in MAS.
📝 Abstract
AI agents need to be robust to unexpected changes in their environment in order to safely operate in real-world scenarios. While some work has been done on this type of robustness in the single-agent case, in this work we introduce the idea that collaboration with other agents can help agents adapt to environment perturbations in multi-agent reinforcement learning settings. We first formalize this notion of resilience of a group of agents. We then empirically evaluate different collaboration protocols and examine their effect on resilience. We see that all of the collaboration approaches considered lead to greater resilience compared to baseline, in line with our hypothesis. We discuss future direction and the general relevance of the concept of resilience introduced in this work.
