Summary of Model-free Low-rank Reinforcement Learning Via Leveraged Entry-wise Matrix Estimation, by Stefan Stojanovic et al.
Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation
by Stefan Stojanovic, Yassir Jedra, Alexandre Proutiere
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 LoRa-PI, a model-free learning algorithm for controlled dynamical systems with low-rank latent structure. The algorithm alternates between policy improvement and evaluation steps, leveraging a two-phase procedure to estimate the low-rank matrix corresponding to the state-action value function. This allows LoRa-PI to learn an ε-optimal policy using Õ(S+A/poly(1-γ)ε^2) samples, outperforming previous approaches under milder conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new algorithm called LoRa-PI that helps computers learn how to make good decisions in complex situations. It uses a special way of looking at data to figure out the most important parts and then makes decisions based on those parts. This algorithm is very efficient and can learn quickly, which is useful for making fast decisions. |
Keywords
* Artificial intelligence * Lora
