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<p>Many real-time problems can be characterized as combinatorial optimization problems where exact solutions are infeasible at scale. As problem complexity grows, handcrafted heuristics become increasingly difficult to design. Reinforcement learning (RL) has emerged as a promising alternative, enabling the discovery of decision-making policies without requiring explicit supervision. While RL does not guarantee optimality, it provides adaptive heuristics to solve complex problems. This paper explores the potential of RL for real-time resource management, outlining key principles, demonstrating an application to directed acyclic graph (DAG) scheduling, and identifying open challenges for future research.</p>

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Position paper: deep reinforcement learning for real-time resource management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月5日

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  • Mirco Theile,
  • Binqi Sun,
  • Marco Caccamo

摘要

Many real-time problems can be characterized as combinatorial optimization problems where exact solutions are infeasible at scale. As problem complexity grows, handcrafted heuristics become increasingly difficult to design. Reinforcement learning (RL) has emerged as a promising alternative, enabling the discovery of decision-making policies without requiring explicit supervision. While RL does not guarantee optimality, it provides adaptive heuristics to solve complex problems. This paper explores the potential of RL for real-time resource management, outlining key principles, demonstrating an application to directed acyclic graph (DAG) scheduling, and identifying open challenges for future research.