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arXiv AI By Gabriel Nova, Stephane Hess, Sander Van Cranenburgh

Multitask Reinforcement Learning for Assisting Choice Model Specification

Read the original on arXiv AI →

Delphos is a multitask reinforcement learning framework that automates discrete choice model specification by treating it as a sequential decision-making problem. It learns transferable specification strategies across multiple transport choice datasets using a DeepSet‑Q architecture, enabling a shared policy to adapt to varying variable sets. Trained on nine datasets, Delphos outperforms single‑task agents, and when applied to unseen Swissmetro and Decisions datasets, it quickly identifies competitive specifications with higher log‑likelihoods than existing methods.

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