Summary of Tight Finite Time Bounds Of Two-time-scale Linear Stochastic Approximation with Markovian Noise, by Shaan Ul Haque et al.
Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise
by Shaan Ul Haque, Sajad Khodadadian, Siva Theja Maguluri
First submitted to arxiv on: 31 Dec 2023
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 This paper focuses on stochastic approximation (SA), an iterative algorithm used in optimization and Reinforcement Learning (RL) to find the fixed point of an operator. When applied to RL algorithms, SA encounters Markovian noise in its updates. The combination of this noise with a two-time-scale structure makes analyzing the algorithm theoretically challenging. This study derives a tight convergence bound for linear two-time-scale SA with Markovian noise, exploring the impact of different step sizes on convergence. By applying these results to the TDC algorithm, the authors demonstrate an improved sample complexity of O(1/ε), outperforming previous work. The proposed approach can also be used to analyze other RL algorithms, such as TD-learning with Polyak averaging, GTD, and GTD2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a way to make machines learn faster and more accurately. It’s called stochastic approximation, or SA for short. When we use SA in real-life situations, like helping computers learn from mistakes, it can get complicated because of the noise that comes into play. The scientists in this study figured out how to measure just how well SA works, even when there’s a lot of noise involved. They showed that their method can be used to make certain computer programs work better, and they think it could help other machine learning algorithms too. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Reinforcement learning
