标题： 超越训练：通过自适应动作采样优化基于强化学习的车间调度 显示英文标题

标题： Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling

摘要： 基于学习的调度问题构造启发式方法近年来已变得与传统求解器和启发式方法越来越具有竞争力。 特别是，使用深度强化学习（DRL）的解决方案方法取得了显著改进。 虽然已经投入了大量精力来设计网络架构和训练算法以实现最先进的结果，但很少有研究探讨在推理过程中最优地使用训练好的DRL代理。 我们的工作基于一个假设，即类似于搜索算法，训练好的DRL代理的使用应取决于可接受的计算预算。 我们提出了一种简单而有效的参数化方法，称为$\delta$-sampling，它通过操纵训练好的动作向量，在解决方案构建过程中使代理行为偏向探索或利用。 通过遵循这种方法，我们可以在仍然生成可接受数量的解决方案的同时，更全面地覆盖搜索空间。 此外，我们提出了一种算法，用于获得给定数量的解决方案和任何训练好的代理的最佳参数化方法。 实验扩展了现有作业车间调度问题的训练协议，并使用我们的推理方法验证了我们的假设，并导致生成的解决方案出现预期的改进。 显示英文摘要

摘要： Learned construction heuristics for scheduling problems have become increasingly competitive with established solvers and heuristics in recent years. In particular, significant improvements have been observed in solution approaches using deep reinforcement learning (DRL). While much attention has been paid to the design of network architectures and training algorithms to achieve state-of-the-art results, little research has investigated the optimal use of trained DRL agents during inference. Our work is based on the hypothesis that, similar to search algorithms, the utilization of trained DRL agents should be dependent on the acceptable computational budget. We propose a simple yet effective parameterization, called $\delta$-sampling that manipulates the trained action vector to bias agent behavior towards exploration or exploitation during solution construction. By following this approach, we can achieve a more comprehensive coverage of the search space while still generating an acceptable number of solutions. In addition, we propose an algorithm for obtaining the optimal parameterization for such a given number of solutions and any given trained agent. Experiments extending existing training protocols for job shop scheduling problems with our inference method validate our hypothesis and result in the expected improvements of the generated solutions.