Risk-averse formulations of Stochastic Optimal Control and Markov Decision Processes
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915298152546304 |
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| author | Shapiro, Alexander Li, Yan |
| author_facet | Shapiro, Alexander Li, Yan |
| contents | The aim of this paper is to investigate risk-averse and distributionally robust modeling of Stochastic Optimal Control (SOC) and Markov Decision Process (MDP). We discuss construction of conditional nested risk functionals, a particular attention is given to the Value-at-Risk measure. Necessary and sufficient conditions for existence of non-randomized optimal policies in the framework of robust SOC and MDP are derived. We also investigate sample complexity of optimization problems involving the Value-at-Risk measure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16651 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Risk-averse formulations of Stochastic Optimal Control and Markov Decision Processes Shapiro, Alexander Li, Yan Optimization and Control Statistics Theory The aim of this paper is to investigate risk-averse and distributionally robust modeling of Stochastic Optimal Control (SOC) and Markov Decision Process (MDP). We discuss construction of conditional nested risk functionals, a particular attention is given to the Value-at-Risk measure. Necessary and sufficient conditions for existence of non-randomized optimal policies in the framework of robust SOC and MDP are derived. We also investigate sample complexity of optimization problems involving the Value-at-Risk measure. |
| title | Risk-averse formulations of Stochastic Optimal Control and Markov Decision Processes |
| topic | Optimization and Control Statistics Theory |
| url | https://arxiv.org/abs/2505.16651 |