Martin Hernandez, Xinyan Qing, Mo Zhou, Yuhua Zhu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28580 · open-access PDF
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 first establish a performance difference lemma and derive policy gradient formulas for randomized and parametrized policies. We then prove exponential convergence of an entropy-regularized policy flow to the regularized optimum. For discrete parametrized updates, we derive a convergence bound that exp
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