Summary of On Bellman Equations For Continuous-time Policy Evaluation I: Discretization and Approximation, by Wenlong Mou et al.
On Bellman equations for continuous-time policy evaluation I: discretization and approximation
by Wenlong Mou, Yuhua Zhu
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA); Optimization and Control (math.OC); Probability (math.PR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel class of algorithms that enable computing value functions from discretely-observed trajectories of continuous-time diffusion processes. The proposed approach leverages numerical schemes compatible with discrete-time reinforcement learning (RL) and function approximation, offering high-order numerical accuracy and bounded approximation error guarantees. Unlike traditional RL problems where the approximation factor depends on the effective horizon, this method exploits underlying elliptic structures to achieve a bounded approximation factor even when the effective horizon diverges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, scientists are working on a new way to calculate values from observations of something that changes over time. They created a set of rules (algorithms) that work well with existing ways of learning from small steps. This new approach is more accurate and reliable than previous methods, even when dealing with very long periods of observation. |
Keywords
* Artificial intelligence * Diffusion * Reinforcement learning
