  Learned construction heuristics for scheduling problems have become
increasingly competitive with established solvers and heuristics in recent
years. In particular, significant improvements have been observed in solution
approaches using deep reinforcement learning (DRL). While much attention has
been paid to the design of network architectures and training algorithms to
achieve state-of-the-art results, little research has investigated the optimal
use of trained DRL agents during inference. Our work is based on the hypothesis
that, similar to search algorithms, the utilization of trained DRL agents
should be dependent on the acceptable computational budget. We propose a simple
yet effective parameterization, called $\delta$-sampling that manipulates the
trained action vector to bias agent behavior towards exploration or
exploitation during solution construction. By following this approach, we can
achieve a more comprehensive coverage of the search space while still
generating an acceptable number of solutions. In addition, we propose an
algorithm for obtaining the optimal parameterization for such a given number of
solutions and any given trained agent. Experiments extending existing training
protocols for job shop scheduling problems with our inference method validate
our hypothesis and result in the expected improvements of the generated
solutions.
