Reinforcement Learning Framework For Stochastic Optimal Control Problem Under Model Uncertainty
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917070594113536 |
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| author | Hou, Jiaxuan Wei, Lifeng |
| author_facet | Hou, Jiaxuan Wei, Lifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06881 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning Framework For Stochastic Optimal Control Problem Under Model Uncertainty Hou, Jiaxuan Wei, Lifeng Optimization and Control 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. |
| title | Reinforcement Learning Framework For Stochastic Optimal Control Problem Under Model Uncertainty |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2511.06881 |