Summary of The Evolution Of Reinforcement Learning in Quantitative Finance: a Survey, by Nikolaos Pippas et al.
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey
by Nikolaos Pippas, Cagatay Turkay, Elliot A. Ludvig
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
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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 presents a comprehensive survey of 167 publications on Reinforcement Learning (RL) in finance, exploring diverse applications and frameworks. The authors evaluate RL’s potential in financial markets, which are characterized by complexity, multi-agent interactions, information asymmetry, and randomness. By incorporating machine learning methods like transfer learning, meta-learning, and multi-agent solutions, RL advances traditional finance with a more dynamic approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The survey dissects key RL components through the lens of Quantitative Finance, uncovering emerging themes and proposing areas for future research. The authors also critique the strengths and weaknesses of existing methods in RL applications within finance. |
Keywords
» Artificial intelligence » Machine learning » Meta learning » Reinforcement learning » Transfer learning
