  Engineering problems that are modeled using sophisticated mathematical
methods or are characterized by expensive-to-conduct tests or experiments, are
encumbered with limited budget or finite computational resources. Moreover,
practical scenarios in the industry, impose restrictions, based on logistics
and preference, on the manner in which the experiments can be conducted. For
example, material supply may enable only a handful of experiments in a
single-shot or in the case of computational models one may face significant
wait-time based on shared computational resources. In such scenarios, one
usually resorts to performing experiments in a manner that allows for
maximizing one's state-of-knowledge while satisfying the above mentioned
practical constraints. Sequential design of experiments (SDOE) is a popular
suite of methods, that has yielded promising results in recent years across
different engineering and practical problems. A common strategy, that leverages
Bayesian formalism is the Bayesian SDOE, which usually works best in the
one-step-ahead or myopic scenario of selecting a single experiment at each step
of a sequence of experiments. In this work, we aim to extend the SDOE strategy,
to query the experiment or computer code at a batch of inputs. To this end, we
leverage deep reinforcement learning (RL) based policy gradient methods, to
propose batches of queries that are selected taking into account entire budget
in hand. The algorithm retains the sequential nature, inherent in the SDOE,
while incorporating elements of reward based on task from the domain of deep
RL. A unique capability of the proposed methodology is its ability to be
applied to multiple tasks, for example optimization of a function, once its
trained. We demonstrate the performance of the proposed algorithm on a
synthetic problem, and a challenging high-dimensional engineering problem.
