Summary of Robust Reinforcement Learning with Dynamic Distortion Risk Measures, by Anthony Coache et al.
Robust Reinforcement Learning with Dynamic Distortion Risk Measures
by Anthony Coache, Sebastian Jaimungal
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Finance (q-fin.CP); Portfolio Management (q-fin.PM); Risk Management (q-fin.RM); Machine Learning (stat.ML)
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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 In this paper, researchers develop a framework for solving reinforcement learning (RL) problems that takes into account both environmental uncertainty and risk preferences. The approach uses dynamic robust distortion risk measures to evaluate policy decisions and introduces robustness by considering all possible models within a certain range of uncertainty around a reference model. The algorithm combines neural networks with policy gradient formulae to optimize decision-making, and is demonstrated on a portfolio allocation example. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better in uncertain situations by taking into account how much risk they’re willing to take. It’s like making smart investment decisions. The researchers create a new way for computers to make choices that considers different possible outcomes and how likely each one is. This approach can be used in many real-world applications, such as managing financial portfolios. |
Keywords
* Artificial intelligence * Reinforcement learning
