Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients

• URL: http://arxiv.org/abs/2406.15612v2
• Date: Fri, 28 Jun 2024 14:23:49 GMT
• Title: Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients
• Authors: Parisa Davar, Frédéric Godin, Jose Garrido,
• Abstract summary: This paper tackles the problem of mitigating catastrophic risk in a sequential decision making process. A policy gradient algorithm is developed, that we call POTPG. An application to financial risk management is presented.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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