Summary of Reinforcement Learning For Jump-diffusions, with Financial Applications, by Xuefeng Gao et al.
Reinforcement Learning for Jump-Diffusions, with Financial Applications
by Xuefeng Gao, Lingfei Li, Xun Yu Zhou
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Mathematical Finance (q-fin.MF)
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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 explores continuous-time reinforcement learning (RL) for stochastic control in systems governed by jump-diffusion processes. It formulates an entropy-regularized exploratory control problem with stochastic policies to balance exploration and exploitation. The paper shows that existing policy evaluation and Q-learning algorithms can be used without modification, but notes that jumps should affect actor and critic parameterizations in general. An application is demonstrated in mean-variance portfolio selection with a jump-diffusion stock price model, where both RL algorithms and parameterizations are invariant to jumps. Finally, the paper presents a study on applying the theory to option hedging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machines learn from their environment when things don’t happen in a straightforward way. It’s like trying to make good decisions when some unexpected events might occur. The researchers found that they can use the same methods as before, but with some extra considerations for these unexpected events. They tested this on a problem where you need to decide how to invest your money, and it worked well even when there were surprises in the market. This is useful because it means we can apply similar ideas to other problems where things don’t always go as planned. |
Keywords
» Artificial intelligence » Diffusion » Reinforcement learning
