Multitask Reinforcement Learning for Assisting Choice Model Specification
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 ...
| Authors | Gabriel Nova, Stephane Hess, Sander Van Cranenburgh |
|---|---|
| Primary category | econ.GN |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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