Summary of A Random Measure Approach to Reinforcement Learning in Continuous Time, by Christian Bender and Nguyen Tran Thuan
A random measure approach to reinforcement learning in continuous time
by Christian Bender, Nguyen Tran Thuan
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Probability (math.PR); 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 A random measure approach is proposed for modeling exploration in continuous-time reinforcement learning with controlled diffusion and jumps. The model uses Brownian motion, Poisson random measures, and additional random variables to simulate control execution on a discrete-time grid. A stochastic differential equation (SDE) is reformulated as an equation driven by suitable random measures. A limit theorem is proven for these random measures as the mesh-size of the sampling grid approaches zero, resulting in a grid-sampling limit SDE that can substitute recent continuous-time RL literature for exploratory control problems and learning algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Exploration in continuous-time reinforcement learning is like navigating through a complex maze. The paper introduces a new way to model this exploration using random measures. Think of it like a map that helps us understand how to make the best decisions. By combining different types of noise, like Brownian motion and Poisson random measures, we can create a more realistic simulation of control execution. This new approach can be used to analyze complex problems and develop better learning algorithms. |
Keywords
» Artificial intelligence » Diffusion » Reinforcement learning
