Randomized Transport Maps for Model-Free Policy-Gradient Mean-Field Control
We develop a model-free policy gradient method for discrete-time mean-field control (MFC). In MFC, the policy affects the objective both through the controlled dynamics and through the population distribution. Standard REINFORCE estimators capture the first effect but not the second. We introduce Transport REINFORCE, a transport map-based approach that perturbs a suitable transformation of the population distribution to estimate this missing mean-field contribution. The method applies to both finite and continuous state spaces. In finite state spaces, we perturb the population distribution directly on the probability simplex through a convex combination of the current population weights a...
| Authors | Adonis Jamal, Samy Mekkaoui, Yadh Hafsi et al. |
|---|---|
| Primary category | math.OC |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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