Summary of Span-based Optimal Sample Complexity For Weakly Communicating and General Average Reward Mdps, by Matthew Zurek et al.
Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs
by Matthew Zurek, Yudong Chen
First submitted to arxiv on: 18 Mar 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 In this paper, researchers investigate the sample complexity of learning an optimal policy in average-reward Markov decision processes (MDPs) under generative models. They establish a minimax-optimal bound for weakly communicating MDPs and generalize their results to multichain MDPs. The study reduces the average-reward MDP to a discounted MDP, requiring novel ideas and improved bounds for -discounted MDPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to learn optimal policies in complex decision-making systems called Markov decision processes (MDPs). By using special models that can generate new data, the researchers figure out how many examples are needed to make accurate decisions. They find a way to solve this problem for two types of MDPs and show that their method is better than existing approaches. |
