Markov Decision Process Design for Imitation of Optimal Task Schedulers.

Due to the generally prohibitive computational requirements of optimal task schedulers, much of the field of task scheduling focuses on designing fast suboptimal algorithms. Since the tree search commonly used by sequencing algorithms such as Branch-and-Bound can naturally be framed as a Markov decision process, designing schedulers using imitation and reinforcement learning is a promising and active area of research. This paper demonstrates how polices can be trained on previously solved scheduling problems and successfully generalize to novel ones. Instead of focusing on policy design, however, this work focuses on designing the Markov decision process observation and reward functions to make learning as effective and efficient as possible. This can be of critical importance when training data is limited or when only simple, fast policies are practical. Various Markov decision process designs are introduced and simulation examples demonstrate the resultant increases in policy performance, even without integration into search algorithms.