  Recent years have seen a rise in interest in terms of using machine learning,
particularly reinforcement learning (RL), for production scheduling problems of
varying degrees of complexity. The general approach is to break down the
scheduling problem into a Markov Decision Process (MDP), whereupon a simulation
implementing the MDP is used to train an RL agent. Since existing studies rely
on (sometimes) complex simulations for which the code is unavailable, the
experiments presented are hard, or, in the case of stochastic environments,
impossible to reproduce accurately. Furthermore, there is a vast array of RL
designs to choose from. To make RL methods widely applicable in production
scheduling and work out their strength for the industry, the standardization of
model descriptions - both production setup and RL design - and validation
scheme are a prerequisite. Our contribution is threefold: First, we standardize
the description of production setups used in RL studies based on established
nomenclature. Secondly, we classify RL design choices from existing
publications. Lastly, we propose recommendations for a validation scheme
focusing on reproducibility and sufficient benchmarking.
