能说话的调度：一种可解释的程序化强化学习框架
Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
AI总结：
本文提出了一种可解释的程序化强化学习框架ProRL，通过人类可读且可编辑的程序化策略实现高效调度，解决了传统深度强化学习在透明性和计算效率方面的不足。
摘要：
深度强化学习（DRL）最近涌现出作为求解组合优化问题（如作业车间调度）的有希望的方法。然而，DRL学习的策略通常由深度神经网络（DNNs）表示，其不透明的神经架构和不可解释的策略决策可能引起人类决策者的关键信任和可用性问题。此外，DNNs的计算需求还会进一步阻碍在资源受限环境中实际部署。在本工作中，我们提出ProRL，一种新颖的可解释程序化强化学习框架，能够通过人类可读且可编辑的程序化策略实现高性能调度（即程序）。我们首先介绍了一种用于调度的领域特定语言（DSL-S）来表示调度策略为结构化程序。ProRL然后通过局部搜索探索由DSL-S定义的程序空间，以识别不完整的程序，这些程序随后通过贝叶斯优化学习其参数。ProRL学习选择哪种调度启发式规则，因此它自然地整合了已在工业场景中使用的现有启发式方法。在广泛使用的基准实例上的实验表明，ProRL在现有启发式方法和DRL基线方面表现出色。此外，ProRL在强约束计算资源下表现良好，例如仅使用100个episode进行训练。我们的代码可在https://github.com/HcPlu/ProRL上获得。
英文摘要：
Deep reinforcement learning (DRL) has recently emerged as a promising approach to solve combinatorial optimization problems such as job shop scheduling. However, the policies learned by DRL are typically represented by deep neural networks (DNNs), whose opaque neural architectures and non-interpretable policy decisions can lead to critical trust and usability concerns for human decision makers. In addition, the computational requirements of DNNs can further hinder practical deployment in resource constrained environments. In this work, we propose ProRL, a novel interpretable programmatic reinforcement learning framework that achieves high-performance scheduling with human-readable and editable programmatic policies (i.e., programs). We first introduce a domain-specific language for scheduling (DSL-S) to represent scheduling strategies as structured programs. ProRL then explores the program space defined by DSL-S using local search to identify incomplete programs, which are subsequently completed by learning their parameters via Bayesian optimization. ProRL learns which scheduling heuristic rules to select, and hence, it naturally incorporates existing heuristics already used in industrial scenarios. Experiments on widely used benchmark instances demonstrate the strong performance of ProRL against existing heuristics and DRL baselines. Furthermore, ProRL performs well under strongly constrained computational resources, such as training with only 100 episodes. Our code is available at https://github.com/HcPlu/ProRL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
