Model-Free Inference of Investor Preferences: A Relative Entropy IRL Approach
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910169237028864 |
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| author | Xu, Chen |
| author_facet | Xu, Chen |
| contents | We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment actions and market conditions. Unlike traditional IRL algorithms, RE-IRL is employed to account for environments where transition probabilities are unknown or inaccessible. To address the challenge of data sparsity, we utilize a $K$-nearest neighbor approach to estimate the observed behavior policy. Furthermore, we propose a statistical testing framework to evaluate the validity and robustness of the estimated results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24280 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Model-Free Inference of Investor Preferences: A Relative Entropy IRL Approach Xu, Chen Machine Learning We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment actions and market conditions. Unlike traditional IRL algorithms, RE-IRL is employed to account for environments where transition probabilities are unknown or inaccessible. To address the challenge of data sparsity, we utilize a $K$-nearest neighbor approach to estimate the observed behavior policy. Furthermore, we propose a statistical testing framework to evaluate the validity and robustness of the estimated results. |
| title | Model-Free Inference of Investor Preferences: A Relative Entropy IRL Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.24280 |