Summary of Catastrophic-risk-aware Reinforcement Learning with Extreme-value-theory-based Policy Gradients, by Parisa Davar et al.
Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients
by Parisa Davar, Frédéric Godin, Jose Garrido
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Risk Management (q-fin.RM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel policy gradient algorithm called POTPG to mitigate catastrophic risks in sequential decision-making processes. The algorithm uses approximations from extreme value theory to tackle the scarcity of observations in the far tail of cumulative costs’ distribution. Numerical experiments demonstrate the out-performance of POTPG over common benchmarks, which rely on empirical distributions. The paper also presents an application to financial risk management, specifically dynamic hedging of a financial option. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to prevent really bad things from happening in decision-making processes. It’s hard because these bad events are rare but very severe. A new algorithm called POTPG helps by using special math techniques to understand the risks. This algorithm works better than other methods, which just look at what usually happens. The paper shows how this can be used to make financial decisions that reduce risk. |
