5D Sigmoid and Piecewise Linear benchmark

该数据集是一个模拟了耦合动作维度与重要性差异（CANDID）属性的基准，用于评估动态算法配置中的顺序策略。该数据集包含了不同配置的动作维度和离散的动作选择，这使得研究者能够评估方法的可扩展性。在5维动作尺度的任务中，该数据集旨在比较顺序策略与基准方法（如DDQN和IQL）在CANDID动作空间中的性能表现。

This dataset is a benchmark simulating the Coupled Action Dimension and Importance Discrepancy (CANDID) property, designed for evaluating sequential policies in dynamic algorithm configuration. It includes diversified configurations of action dimensions and discrete action selections, enabling researchers to assess the scalability of their methods. For tasks with 5-dimensional action spaces, this dataset aims to compare the performance of sequential policies against baseline methods such as DDQN and IQL in the CANDID action space.