System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy. (arXiv:2201.07051v2 [cs.LG] UPDATED)

Dynamic scheduling is an important problem in applications from queuing to
wireless networks. It addresses how to choose an item among multiple scheduling
items in each timestep to achieve a long-term goal. Conventional approaches for
dynamic scheduling find the optimal policy for a given specific system so that
the policy from these approaches is usable only for the corresponding system
characteristics. Hence, it is hard to use such approaches for a practical
system in which system characteristics dynamically change. This …

arxiv learning meta meta-learning policy scheduling