Reinforcement Learning Framework For Stochastic Optimal Control Problem Under Model Uncertainty

Jiaxuan Hou; Lifeng Wei

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arXiv · 2511.06881

Reinforcement Learning Framework For Stochastic Optimal Control Problem Under Model Uncertainty

Jiaxuan Hou·Lifeng Wei

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Abstract

We develop a continuous-time entropy-regularized reinforcement learning framework under model uncertainty. By applying Sion's minimax theorem, we transform the intractable robust control problem into an equivalent standard entropy-regularized stochastic control problem, facilitating reinforcement learning algorithms. We establish sufficient conditions for the theorem's validity and demonstrate our approach on linear-quadratic problems with uncertain model parameters following Bernoulli and uniform distributions.

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BibTeX RIS

Jiaxuan Hou, Lifeng Wei. 2025-11-10. Reinforcement Learning Framework For Stochastic Optimal Control Problem Under Model Uncertainty. https://arxiv.org/abs/2511.06881

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