Summary of Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation, by Yixuan Zhang and Qiaomin Xie
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation
by Yixuan Zhang, Qiaomin Xie
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 investigates asynchronous Q-learning with constant stepsize, a widely used algorithm in reinforcement learning (RL). The authors show that this algorithm converges in Wasserstein distance and establish an exponential convergence rate. They also demonstrate the asymptotic normality of the averaged iterates using Central Limit Theory. Furthermore, they provide an explicit expansion of the asymptotic bias of the averaged iterate, which is proportional to the stepsize up to higher-order terms. This precise characterization allows for the application of Richardson-Romberg (RR) extrapolation technique to construct a new estimate that is provably closer to the optimal Q function. The paper’s findings are corroborated by numerical results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make Q-learning, a type of algorithm used in artificial intelligence, work better. They find a way to make it more accurate and faster by using something called constant stepsize. They also show that the results get closer to the correct answer as you take more steps. This is important because it helps us understand how AI algorithms can be improved. |
Keywords
* Artificial intelligence * Reinforcement learning
