Continuous-Time Policy Gradient from Discrete Data: Convergence Analysis and Model-Free Algorithms

Continuous-time reinforcement learning seeks an optimal policy under unknown dynamics driven by stochastic differential equations using observations collected at discrete times. We develop a policy gradient framework that combines rigorous convergence analysis with learning from discrete data. We fi