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| Format: | Preprint |
| Published: |
2024
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| Online Access: | https://arxiv.org/abs/2407.13189 |
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| _version_ | 1866916329279193088 |
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| author | Moustakides, George V. |
| author_facet | Moustakides, George V. |
| contents | When the underlying conditional density is known, conditional expectations can be computed analytically or numerically. When, however, such knowledge is not available and instead we are given a collection of training data, the goal of this work is to propose simple and purely data-driven means for estimating directly the desired conditional expectation. Because conditional expectations appear in the description of a number of stochastic optimization problems with the corresponding optimal solution satisfying a system of nonlinear equations, we extend our data-driven method to cover such cases as well. We test our methodology by applying it to Optimal Stopping and Optimal Action Policy in Reinforcement Learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13189 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Estimation of Conditional Expectations, Application to Optimal Stopping and Reinforcement Learning Moustakides, George V. Machine Learning 60J20, 68T07 When the underlying conditional density is known, conditional expectations can be computed analytically or numerically. When, however, such knowledge is not available and instead we are given a collection of training data, the goal of this work is to propose simple and purely data-driven means for estimating directly the desired conditional expectation. Because conditional expectations appear in the description of a number of stochastic optimization problems with the corresponding optimal solution satisfying a system of nonlinear equations, we extend our data-driven method to cover such cases as well. We test our methodology by applying it to Optimal Stopping and Optimal Action Policy in Reinforcement Learning. |
| title | Data-Driven Estimation of Conditional Expectations, Application to Optimal Stopping and Reinforcement Learning |
| topic | Machine Learning 60J20, 68T07 |
| url | https://arxiv.org/abs/2407.13189 |