  We propose a novel adaptive reinforcement learning control approach for fault
tolerant control of degrading systems that is not preceded by a fault detection
and diagnosis step. Therefore, \textit{a priori} knowledge of faults that may
occur in the system is not required. The adaptive scheme combines online and
offline learning of the on-policy control method to improve exploration and
sample efficiency, while guaranteeing stable learning. The offline learning
phase is performed using a data-driven model of the system, which is frequently
updated to track the system's operating conditions. We conduct experiments on
an aircraft fuel transfer system to demonstrate the effectiveness of our
approach.
