  Cooperative multi-agent reinforcement learning (MARL) approaches tackle the
challenge of finding effective multi-agent cooperation strategies for
accomplishing individual or shared objectives in multi-agent teams. In
real-world scenarios, however, agents may encounter unforeseen failures due to
constraints like battery depletion or mechanical issues. Existing
state-of-the-art methods in MARL often recover slowly -- if at all -- from such
malfunctions once agents have already converged on a cooperation strategy. To
address this gap, we present the Collaborative Adaptation (CA) framework. CA
introduces a mechanism that guides collaboration and accelerates adaptation
from unforeseen failures by leveraging inter-agent relationships. Our findings
demonstrate that CA enables agents to act on the knowledge of inter-agent
relations, recovering from unforeseen agent failures and selecting appropriate
cooperative strategies.
