  Faults are endemic to all systems. Adaptive fault-tolerant control maintains
degraded performance when faults occur as opposed to unsafe conditions or
catastrophic events. In systems with abrupt faults and strict time constraints,
it is imperative for control to adapt quickly to system changes to maintain
system operations. We present a meta-reinforcement learning approach that
quickly adapts its control policy to changing conditions. The approach builds
upon model-agnostic meta learning (MAML). The controller maintains a complement
of prior policies learned under system faults. This "library" is evaluated on a
system after a new fault to initialize the new policy. This contrasts with
MAML, where the controller derives intermediate policies anew, sampled from a
distribution of similar systems, to initialize a new policy. Our approach
improves sample efficiency of the reinforcement learning process. We evaluate
our approach on an aircraft fuel transfer system under abrupt faults.
