  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 paper proposes
a novel policy structure for MDP-based dynamic scheduling, a descriptive
policy, which has a system-agnostic capability to adapt to unseen system
characteristics for an identical task (dynamic scheduling). To this end, the
descriptive policy learns a system-agnostic scheduling principle--in a
nutshell, "which condition of items should have a higher priority in
scheduling". The scheduling principle can be applied to any system so that the
descriptive policy learned in one system can be used for another system.
Experiments with simple explanatory and realistic application scenarios
demonstrate that it enables system-agnostic meta-learning with very little
performance degradation compared with the system-specific conventional
policies.
