Summary of Span-based Optimal Sample Complexity For Average Reward Mdps, by Matthew Zurek et al.
Span-Based Optimal Sample Complexity for Average Reward MDPs
by Matthew Zurek, Yudong Chen
First submitted to arxiv on: 22 Nov 2023
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 This paper studies the sample complexity of learning an epsilon-optimal policy in average-reward Markov decision processes (MDPs) under a generative model. The authors establish a complexity bound, specifically O(SA*H/epsilon^2), where H is the span of the bias function of the optimal policy and SA is the cardinality of the state-action space. This result is minimax optimal in all parameters S, A, H, and epsilon, improving on existing work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to learn good decisions in complex systems. It’s like trying to find the best way to get from point A to point B in a big city. The researchers figured out a new way to measure how much data we need to make these kinds of decisions. Their method is the most efficient one yet, which means it can help us learn and improve faster. |
Keywords
* Artificial intelligence * Generative model
