  While conventional reinforcement learning focuses on designing agents that
can perform one task, meta-learning aims, instead, to solve the problem of
designing agents that can generalize to different tasks (e.g., environments,
obstacles, and goals) that were not considered during the design or the
training of these agents. In this spirit, in this paper, we consider the
problem of training a provably safe Neural Network (NN) controller for
uncertain nonlinear dynamical systems that can generalize to new tasks that
were not present in the training data while preserving strong safety
guarantees. Our approach is to learn a set of NN controllers during the
training phase. When the task becomes available at runtime, our framework will
carefully select a subset of these NN controllers and compose them to form the
final NN controller. Critical to our approach is the ability to compute a
finite-state abstraction of the nonlinear dynamical system. This abstract model
captures the behavior of the closed-loop system under all possible NN weights,
and is used to train the NNs and compose them when the task becomes available.
We provide theoretical guarantees that govern the correctness of the resulting
NN. We evaluated our approach on the problem of controlling a wheeled robot in
cluttered environments that were not present in the training data.
