works on
continuous-time reinforcement learning 1deterministic policy gradient 1McKean-Vlasov dynamics 1mean field control 1particle methods 1

From the 1 of 4 linked papers with an AI index.

collaborators

4 papers

math.OC2026

Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies

Ziheng Cheng, Xin Guo, Huyên Pham +1

The paper proposes a model‑free reinforcement learning framework for continuous‑time extended mean field control using deterministic feedback policies, deriving deterministic polic…

math.OC2026

Policy Gradient Learning for Distributionally Robust Markov Decision Processes under Wasserstein Ambiguity

Yadh Hafsi, Samy Mekkaoui, Huyên Pham +1

We study finite-horizon Markov decision processes under distributional uncertainty in the transition kernels and develop a policy-gradient framework for Wasserstein distributionall…

math.OC2026

Discretization error from regularized Reinforcement Learning to continuous-time stochastic control

Huyên Pham, Yuming Paul Zhang, Yuhua Zhu

This paper establishes a rigorous connection between regularized discrete-time reinforcement learning (RL) and continuous-time stochastic optimal control. Specifically, classical R…

math.OC2026

Model-free policy gradient for discrete-time mean-field control

Matthieu Meunier, Huyên Pham, Christoph Reisinger

We study model-free policy learning for discrete-time mean-field control (MFC) problems with finite state space and compact action space. In contrast to the extensive literature on…