Can RL improve real-time task scheduling?
Improving Mixed-Criticality Scheduling with Reinforcement Learning
This paper explores using reinforcement learning (RL) to schedule tasks with different criticality levels (e.g., high-priority flight control vs. low-priority video rendering) on processors that can vary in speed. It focuses on the difficult "non-preemptive" scheduling problem, where tasks cannot be interrupted once started.
While not explicitly about LLMs, the use of an RL agent to make complex scheduling decisions in a dynamic environment is relevant to LLM-based multi-agent systems. The paper highlights how an RL agent can learn to balance competing priorities (high vs. low criticality tasks) under changing conditions (processor speed), which is directly analogous to coordinating actions among multiple LLM agents with different objectives. The focus on non-preemptive scheduling could also inform scenarios where LLM actions have irreversible consequences.