Papers

• TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning (2025)
  Hongyu Lin et al.
  3.92
• TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs (2026)
  Rong Fu et al.
  1.72
• Meta-Reinforcement Learning with Discrete World Models for Adaptive Load Balancing (2025)
  Cameron Redovian
  1.11
• Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques (2025)
  Chiyu Cheng et al.
  1.00
• DL2: A Deep Learning-driven Scheduler for Deep Learning Clusters (2019)
  Yanghua Peng et al.
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• Associative Memory Based Experience Replay for Deep Reinforcement Learning (2022)
  Mengyuan Li et al.
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• Deep Reinforcement Learning for System-on-Chip: Myths and Realities (2022)
  Tegg Taekyong Sung et al.
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• POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning (2022)
  Shalini Jain et al.
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• CPU frequency scheduling of real-time applications on embedded devices with temporal encoding-based deep reinforcement learning (2023)
  Ti Zhou and Man Lin
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• Energy-Efficient Computation with DVFS using Deep Reinforcement Learning for Multi-Task Systems in Edge Computing (2024)
  Xinyi Li et al.
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• Reinforcement Learning for Dynamic Memory Allocation (2024)
  Arisrei Lim et al.
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• Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-Series Forecasting (2024)
  Manuel Sage and Joshua Campbell and Yaoyao Fiona Zhao
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• Enhancing Adaptive Mixed-Criticality Scheduling with Deep Reinforcement Learning (2024)
  Bruno Mendes (1) et al.
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