Data-Driven Estimation of Conditional Expectations, Application to Optimal Stopping and Reinforcement Learning
<|reference_start|>Data-Driven Estimation of Conditional Expectations, Application to Optimal Stopping and Reinforcement Learning: 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.<|reference_end|>
@article{moustakides2024data-driven,
    title={Data-Driven Estimation of Conditional Expectations, Application to
  Optimal Stopping and Reinforcement Learning},
    author={George V. Moustakides},
    journal={arXiv preprint arXiv:2407.13189},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13189},
    primaryClass={stat.ML cs.LG}
}