  Cooperative multi-agent learning plays a crucial role for developing
effective strategies to achieve individual or shared objectives in multi-agent
teams. In real-world settings, agents may face unexpected failures, such as a
robot's leg malfunctioning or a teammate's battery running out. These
malfunctions decrease the team's ability to accomplish assigned task(s),
especially if they occur after the learning algorithms have already converged
onto a collaborative strategy. Current leading approaches in Multi-Agent
Reinforcement Learning (MARL) often recover slowly -- if at all -- from such
malfunctions. To overcome this limitation, we present the Collaborative
Adaptation (CA) framework, highlighting its unique capability to operate in
both continuous and discrete domains. Our framework enhances the adaptability
of agents to unexpected failures by integrating inter-agent relationships into
their learning processes, thereby accelerating the recovery from malfunctions.
We evaluated our framework's performance through experiments in both discrete
and continuous environments. Empirical results reveal that in scenarios
involving unforeseen malfunction, although state-of-the-art algorithms often
converge on sub-optimal solutions, the proposed CA framework mitigates and
recovers more effectively.
