Summary of Statistical Inference For Temporal Difference Learning with Linear Function Approximation, by Weichen Wu et al.
Statistical Inference for Temporal Difference Learning with Linear Function Approximation
by Weichen Wu, Gen Li, Yuting Wei, Alessandro Rinaldo
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 consistency properties of Temporal Difference (TD) learning with Polyak-Ruppert averaging and linear function approximation in Markov decision processes (MDPs). The authors derive novel guarantees for the value function, including a sharp high-dimensional probability convergence guarantee that depends explicitly on the asymptotic variance. They also establish refined Berry-Esseen bounds over convex sets and propose a plug-in estimator for the asymptotic covariance matrix. These results enable the construction of confidence regions and simultaneous confidence intervals for linear parameters of the value function, with guaranteed finite-sample coverage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps us understand how to make sure our artificial intelligence (AI) is reliable when learning from experience in complex situations called Markov decision processes. The authors study a popular AI algorithm called Temporal Difference (TD) learning and show that it can be used to evaluate the value of different actions in these complex situations. They also develop new methods for estimating the uncertainty of their AI’s predictions, which is important for making reliable decisions. |
Keywords
* Artificial intelligence * Probability
