  This paper describes a purely data-driven solution to a class of sequential
decision-making problems with a large number of concurrent online decisions,
with applications to computing systems and operations research. We assume that
while the micro-level behaviour of the system can be broadly captured by
analytical expressions or simulation, the macro-level or emergent behaviour is
complicated by non-linearity, constraints, and stochasticity. If we represent
the set of concurrent decisions to be computed as a vector, each element of the
vector is assumed to be a continuous variable, and the number of such elements
is arbitrarily large and variable from one problem instance to another. We
first formulate the decision-making problem as a canonical reinforcement
learning (RL) problem, which can be solved using purely data-driven techniques.
We modify a standard approach known as advantage actor critic (A2C) to ensure
its suitability to the problem at hand, and compare its performance to that of
baseline approaches on the specific instance of a multi-product inventory
management task. The key modifications include a parallelised formulation of
the decision-making task, and a training procedure that explicitly recognises
the quantitative relationship between different decisions. We also present
experimental results probing the learned policies, and their robustness to
variations in the data.
