Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909233155407872 |
|---|---|
| author | Davar, Parisa Godin, Frédéric Garrido, Jose |
| author_facet | Davar, Parisa Godin, Frédéric Garrido, Jose |
| contents | This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of observations in the far tail of the distribution of cumulative costs (negative rewards). A policy gradient algorithm is developed, that we call POTPG. It is based on approximations of the tail risk derived from extreme value theory. Numerical experiments highlight the out-performance of our method over common benchmarks, relying on the empirical distribution. An application to financial risk management, more precisely to the dynamic hedging of a financial option, is presented. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15612 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients Davar, Parisa Godin, Frédéric Garrido, Jose Machine Learning Risk Management This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of observations in the far tail of the distribution of cumulative costs (negative rewards). A policy gradient algorithm is developed, that we call POTPG. It is based on approximations of the tail risk derived from extreme value theory. Numerical experiments highlight the out-performance of our method over common benchmarks, relying on the empirical distribution. An application to financial risk management, more precisely to the dynamic hedging of a financial option, is presented. |
| title | Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients |
| topic | Machine Learning Risk Management |
| url | https://arxiv.org/abs/2406.15612 |