Summary of Matrix Low-rank Approximation For Policy Gradient Methods, by Sergio Rozada and Antonio G. Marques
Matrix Low-Rank Approximation For Policy Gradient Methods
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 approach to estimating policies in reinforcement learning by leveraging low-rank matrix-based models. Traditionally, policies are inferred from value functions, but exact computation suffers from the curse of dimensionality. Policy gradient methods learn directly parametric stochastic policies using neural networks tuned via stochastic gradient descent. However, finding adequate network architectures can be challenging, and convergence issues are common. The proposed method collects policy parameters into a matrix and applies matrix-completion techniques to promote low-rank structures. Numerical studies demonstrate that the approach reduces computational and sample complexities relative to traditional neural network models while achieving similar aggregated rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding better ways to make decisions in situations where we don’t know what will happen next. It’s like trying to figure out how to get to a goal when you’re playing a game. Usually, people use “value functions” to decide what to do, but that can be hard when there are many possibilities. Instead, this paper suggests using “policy gradient” methods, which learn directly from the situation and make decisions based on what works best. The problem is that these methods often require complicated neural networks, which can be tricky to set up and may not work well. This paper proposes a new way of doing things by representing the decision-making process as a matrix and using special techniques to keep it simple. It shows that this approach can be more efficient and effective than traditional methods. |
Keywords
» Artificial intelligence » Neural network » Reinforcement learning » Stochastic gradient descent
