Summary of The Plug-in Approach For Average-reward and Discounted Mdps: Optimal Sample Complexity Analysis, by Matthew Zurek et al.
The Plug-in Approach for Average-Reward and Discounted MDPs: Optimal Sample Complexity Analysis
by Matthew Zurek, Yudong Chen
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); 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 paper studies the efficiency of the plug-in approach in learning optimal policies for average-reward Markov decision processes (MDPs) with a generative model. The plug-in approach constructs an estimated model and then computes an optimal policy within it, requiring no prior knowledge or parameter tuning. This method has never been theoretically analyzed before, despite being one of the simplest algorithms for this problem. The paper’s results show that the plug-in approach is optimal in certain settings without using prior information, achieving sample complexities similar to those of other methods. The authors also obtain bounds for the plug-in approach and provide lower bounds suggesting that they are unimprovable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well a simple way of learning policies works for average-reward MDPs with a generative model. A generative model is like a recipe book, telling us what to do in each situation. The plug-in approach is a simple way to find the best policy – it makes an estimate of the MDP and then finds the best policy within that estimate. This method has never been studied before, even though it’s one of the easiest ways to solve this problem. The paper shows that this approach works well in certain situations without needing any special information or adjustments. It also provides new bounds for how well this approach can work. |
Keywords
» Artificial intelligence » Generative model
