Summary of Almost Sure Convergence Rates and Concentration Of Stochastic Approximation and Reinforcement Learning with Markovian Noise, by Xiaochi Qian et al.
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
by Xiaochi Qian, Zixuan Xie, Xinyu Liu, Shangtong Zhang
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 proposed paper presents a novel approach to establishing convergence rates and concentration bounds for general contractive stochastic approximation algorithms with Markovian noise. The authors introduce a new discretization method, using intervals with diminishing length, which enables them to achieve exponential tails in their results. As applications, they demonstrate the first almost sure convergence rate for Q-learning without count-based learning rates, as well as the first concentration bound for off-policy temporal difference learning. Their work has significant implications for the development of reinforcement learning algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can learn and make decisions when given incomplete or noisy information. The authors create a new way to analyze an important type of computer algorithm, which is used in many real-world applications like self-driving cars and personal assistants. They show that this algorithm can be very reliable and efficient by using a special kind of math problem-solving approach. This has big implications for making computers better at learning from experience. |
Keywords
* Artificial intelligence * Reinforcement learning
