System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy

TL;DR

This paper introduces a system-agnostic descriptive policy for MDP-based dynamic scheduling that can adapt to unseen system characteristics, enabling effective meta-learning across different systems with minimal performance loss.

Contribution

It proposes a novel, system-agnostic policy structure for dynamic scheduling that learns a universal scheduling principle transferable across systems.

Findings

Enables system-agnostic meta-learning with minimal performance degradation.

Demonstrates effectiveness in both simple and realistic scenarios.

Outperforms traditional system-specific policies in adaptability.

Abstract

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.