  This paper discusses an Enhanced Model-Agnostic Meta-Learning (E-MAML)
algorithm that generates fast convergence of the policy function from a small
number of training examples when applied to new learning tasks. Built on top of
Model-Agnostic Meta-Learning (MAML), E-MAML maintains a set of policy
parameters learned in the environment for previous tasks. We apply E-MAML to
developing reinforcement learning (RL)-based online fault tolerant control
schemes for dynamic systems. The enhancement is applied when a new fault
occurs, to re-initialize the parameters of a new RL policy that achieves faster
adaption with a small number of samples of system behavior with the new fault.
This replaces the random task sampling step in MAML. Instead, it exploits the
extant previously generated experiences of the controller. The enhancement is
sampled to maximally span the parameter space to facilitate adaption to the new
fault. We demonstrate the performance of our approach combining E-MAML with
proximal policy optimization (PPO) on the well-known cart pole example, and
then on the fuel transfer system of an aircraft.
