mlsanityData to 14 Oct 2026

Adaptive Approach to Enhance Machine Learning Scheduling Algorithms During Runtime Using Reinforcement Learning in Metascheduling Applications

arXiv 2509.20520 ↗First posted 24 Sep 2025cs.AI cs.DC cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Metascheduling in time-triggered architectures has been crucial in adapting to dynamic and unpredictable environments, ensuring the reliability and efficiency of task execution. However, traditional approaches face significant challenges when training Artificial Intelligence (AI) scheduling inferences offline, particularly due to the complexities involved in constructing a comprehensive Multi-Schedule Graph (MSG) that accounts for all possible scenarios. The process of generating an MSG that captures the vast probability space, especially when considering context events like hardware failures, slack variations, or mode changes, is resource-intensive and often infeasible. To address these challenges, we propose an adaptive online learning unit integrated within the metascheduler to enhance performance in real-time. The primary motivation for developing this unit stems from the limitations of offline training, where the MSG created is inherently a subset of the complete space, focusing only on the most probable and critical context events. In the online mode, Reinforcement Learning (RL) plays a pivotal role by continuously exploring and discovering new scheduling solutions, thus expanding the MSG and enhancing system performance over time. This dynamic adaptation allows the system to handle unexpected events and complex scheduling scenarios more effectively. Several RL models were implemented within the online learning unit, each designed to address specific challenges in scheduling. These models not only facilitate the discovery of new solutions but also optimize existing schedulers, particularly when stricter deadlines or new performance criteria are introduced. By continuously refining the AI inferences through real-time training, the system remains flexible and capable of meeting evolving demands, thus ensuring robustness and efficiency in large-scale, safety-critical environments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 34 references that are in the map. First posted on arXiv 24 Sep 2025.

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5 of 34 references.

References · 5

  1. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  2. 2016SNAP: A General Purpose Network Analysis and Graph Mining Librarycited by 56
  3. 2020Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learningcited by 7
  4. 2020Scheduling for Mobile Edge Computing with Random User Arrivals: An Approximate MDP and Reinforcement Learning Approachcited by 4
  5. 2017Metascheduling of HPC Jobs in Day-Ahead Electricity Marketscited by 1

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  2. 2025Improving Mixed-Criticality Scheduling with Reinforcement Learning
  3. 2025Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
  4. 2025Meta-Reinforcement Learning with Discrete World Models for Adaptive Load Balancing
  5. 2025Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling
  6. 2022Launchpad: Learning to Schedule Using Offline and Online RL Methods
  7. 2021Offline Meta-Reinforcement Learning for Industrial Insertion
  8. 2026Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning