  A desirable property in fault-tolerant controllers is adaptability to system
changes as they evolve during systems operations. An adaptive controller does
not require optimal control policies to be enumerated for possible faults.
Instead it can approximate one in real-time. We present two adaptive
fault-tolerant control schemes for a discrete time system based on hierarchical
reinforcement learning. We compare their performance against a model predictive
controller in presence of sensor noise and persistent faults. The controllers
are tested on a fuel tank model of a C-130 plane. Our experiments demonstrate
that reinforcement learning-based controllers perform more robustly than model
predictive controllers under faults, partially observable system models, and
varying sensor noise levels.
