Summary of Almost Sure Convergence Of Linear Temporal Difference Learning with Arbitrary Features, by Jiuqi Wang and Shangtong Zhang
Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
by Jiuqi Wang, Shangtong Zhang
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research paper investigates the convergence of temporal difference (TD) learning with linear function approximation, a fundamental prediction algorithm in reinforcement learning. The study reveals that traditional assumptions about feature independence are not necessary for almost sure convergence, instead offering a novel characterization of bounded invariant sets of the mean ODE of linear TD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper solves a long-standing problem in machine learning by showing that a popular algorithm can work well even when the features used aren’t perfectly independent. This is important because it means we don’t need to make any special assumptions or modify the algorithm to get good results. The study focuses on the linear TD algorithm and its ability to learn from experience and make accurate predictions. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
