Summary of A Simple Finite-time Analysis Of Td Learning with Linear Function Approximation, by Aritra Mitra
A Simple Finite-Time Analysis of TD Learning with Linear Function Approximation
by Aritra Mitra
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC)
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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 the finite-time convergence of Temporal Difference (TD) learning with linear function approximation under Markovian sampling. The authors aim to simplify existing proofs that assume a projection step or require intricate analysis. They propose a two-step argument, first showing that iterates remain uniformly bounded in expectation using induction, and then establishing a recursion that captures the effect of Markovian sampling up to a bounded perturbation. This approach simplifies existing proofs and has potential applications in analyzing more complex stochastic approximation algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how artificial intelligence learns over time. The authors want to make it easier to prove that certain learning methods work well without making big assumptions or doing complicated math. They come up with a new way of proving things, which involves showing two main points: first, the numbers used in the learning process stay within certain limits; and second, the actual learning process behaves similarly to what we would expect if it were happening forever. This new approach makes it easier to understand how these learning methods work and could be useful for other types of machine learning algorithms. |
Keywords
* Artificial intelligence * Machine learning
