发表机构
Delft University of Technology; University of Leeds（代尔夫特理工大学; 利兹大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出多任务强化学习框架Delphos，通过跨数据集共享建模经验，实现离散选择模型的自动设定，减少试错并保持建模者控制。
AI 中文摘要
离散选择模型设定是一项耗时的任务，在此过程中，建模者通常需要设定并估计多个模型，同时平衡拟合优度、简约性和行为合理性。我们提出了Delphos，一个多任务强化学习框架，它能够跨交通选择数据集学习可迁移的设定策略。Delphos将模型设定构建为一个序列决策问题，在其中应用一系列建模动作，并根据模型性能和收敛性从估计环境中接收反馈。为了在不同变量集的数据集之间迁移建模决策，Delphos使用DeepSet-Q架构将效用设定表示为建模项的集合，从而允许共享的设定策略跨多个数据集学习。在九个交通选择数据集上训练后，Delphos始终优于独立训练的单任务智能体，表明共享建模经验能提高学习效率，并有助于以更少的失败估计尝试识别有前景的建模决策序列。当未经进一步训练直接应用于未见过的Swissmetro和Decisions数据集时，同一智能体在标准CPU上不到20分钟内识别出具有竞争力的设定。在Swissmetro上，它实现了比VNS元启发式更高的每观测对数似然，并且在Decisions上，其性能与专家建模者发布的MNL设定相当。这些发现表明，积累和重用建模经验使Delphos能够充当离散选择模型设定的智能助手。它减少了手动试错，同时允许建模者保留对模型诊断、细化和最终选择的控制。
英文摘要
Discrete choice model specification is a time-consuming task in which modellers often specify and estimate multiple models while balancing goodness-of-fit, parsimony, and behavioural plausibility. We present Delphos, a multitask reinforcement learning framework that learns transferable specification strategies across transport choice datasets. Delphos frames model specification as a sequential decision-making problem in which it applies a sequence of modelling actions and receives feedback from an estimation environment based on model performance and convergence. To transfer modelling decisions across datasets with different sets of variables, Delphos represents utility specifications as sets of modelling terms using a DeepSet-Q architecture, allowing a shared specification policy to learn across multiple datasets. Trained on nine transport choice datasets, Delphos consistently outperforms independently trained single-task agents, indicating that sharing modelling experience improves learning efficiency and helps identify promising sequences of modelling decisions with fewer unsuccessful estimation attempts. When applied without further training to the unseen Swissmetro and Decisions datasets, the same agent identifies competitive specifications in less than 20 minutes on a standard CPU. It achieves a higher log-likelihood per observation than the VNS metaheuristic on Swissmetro and performance comparable to a published MNL specification developed by expert modellers on Decisions. These findings show that accumulating and reusing modelling experience enables Delphos to function as an intelligent assistant for discrete choice model specification. It reduces manual trial-and-error while allowing modellers to retain control over model diagnosis, refinement, and final selection.
