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arXiv Machine Learning By Adonis Jamal, Samy Mekkaoui, Yadh Hafsi, Huy\^en Pham

Randomized Transport Maps for Model-Free Policy-Gradient Mean-Field Control

Read the original on arXiv Machine Learning →

The paper introduces Transport REINFORCE, a model‑free policy‑gradient method for discrete‑time mean‑field control that captures both the dynamics and population‑distribution effects of a policy. It perturbs the population distribution—directly on the simplex for finite state spaces and via a Gaussian‑mixture transport map for continuous spaces—to estimate the missing mean‑field contribution. The authors prove consistency of the perturbed objective and gradient, provide bias and mean‑square error bounds, and demonstrate improved performance over standard REINFORCE on several benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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