  Optimal control of parametric partial differential equations (PDEs) is
crucial in many applications in engineering and science. In recent years, the
progress in scientific machine learning has opened up new frontiers for the
control of parametric PDEs. In particular, deep reinforcement learning (DRL)
has the potential to solve high-dimensional and complex control problems in a
large variety of applications. Most DRL methods rely on deep neural network
(DNN) control policies. However, for many dynamical systems, DNN-based control
policies tend to be over-parametrized, which means they need large amounts of
training data, show limited robustness, and lack interpretability. In this
work, we leverage dictionary learning and differentiable L$_0$ regularization
to learn sparse, robust, and interpretable control policies for parametric
PDEs. Our sparse policy architecture is agnostic to the DRL method and can be
used in different policy-gradient and actor-critic DRL algorithms without
changing their policy-optimization procedure. We test our approach on the
challenging tasks of controlling parametric Kuramoto-Sivashinsky and
convection-diffusion-reaction PDEs. We show that our method (1) outperforms
baseline DNN-based DRL policies, (2) allows for the derivation of interpretable
equations of the learned optimal control laws, and (3) generalizes to unseen
parameters of the PDE without retraining the policies.
