Summary of Stochastic Approximation with Unbounded Markovian Noise: a General-purpose Theorem, by Shaan Ul Haque et al.
Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
by Shaan Ul Haque, Siva Theja Maguluri
First submitted to arxiv on: 29 Oct 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 average-reward Reinforcement Learning in unbounded state spaces and reward functions, motivated by applications like network resource allocation and inventory systems. Building on previous work in actor-critic frameworks, this study focuses on Temporal Difference learning with linear function approximation, establishing finite-time bounds with optimal sample complexity of O(1/ε²). These results are achieved through a general-purpose theorem for non-linear Stochastic Approximation (SA). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using machines to learn and make good decisions in situations where there’s no clear reward or punishment. It looks at how this works when the things that can happen are endless, like when managing resources in networks or inventory levels. The researchers found a way to use a type of learning called Temporal Difference learning with linear function approximation, which helps them understand how many tries they need to make before they get the right answer. |
Keywords
* Artificial intelligence * Reinforcement learning
