  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.
