Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients
<|reference_start|>Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients: 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.<|reference_end|>
@article{davar2024catastrophic-risk-aware,
    title={Catastrophic-risk-aware reinforcement learning with
  extreme-value-theory-based policy gradients},
    author={Parisa Davar, Fr'ed'eric Godin, Jose Garrido},
    journal={arXiv preprint arXiv:2406.15612},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15612},
    primaryClass={cs.LG q-fin.RM}
}