On the impact of MDP design for Reinforcement Learning agents in
  Resource Management
  The recent progress in Reinforcement Learning applications to Resource
Management presents MDPs without a deeper analysis of the impacts of design
decisions on agent performance. In this paper, we compare and contrast four
different MDP variations, discussing their computational requirements and
impacts on agent performance by means of an empirical analysis. We conclude by
showing that, in our experiments, when using Multi-Layer Perceptrons as
approximation function, a compact state representation allows transfer of
agents between environments, and that transferred agents have good performance
and outperform specialized agents in 80\% of the tested scenarios, even without
retraining.
