Summary of Tensor Low-rank Approximation Of Finite-horizon Value Functions, by Sergio Rozada and Antonio G. Marques
Tensor Low-rank Approximation of Finite-horizon Value Functions
by Sergio Rozada, Antonio G. Marques
First submitted to arxiv on: 27 May 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 The paper presents a novel non-parametric algorithm for estimating value functions (VFs) in finite-horizon Markov Decision Processes (MDPs). The challenge lies in handling the growing number of VFs with increasing time horizon. The proposed approach represents the VFs as a tensor, where time is one dimension, and uses rewards sampled from the MDP to estimate optimal VFs via truncated PARAFAC decomposition. This online low-rank algorithm efficiently recovers the entries of the VF tensor, demonstrated through numerical experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn how to make good decisions by solving a big problem in a type of math called Markov Decision Processes (MDPs). MDPs have a “reward” system that tells them if their actions are good or bad. The challenge is that the number of possible good and bad outcomes grows as time passes. The researchers came up with a new way to solve this problem using math tricks and computer algorithms. They tested it on some examples and showed it works well. |
