Summary of Model-free Robust Reinforcement Learning with Sample Complexity Analysis, by Yudan Wang et al.
Model-Free Robust Reinforcement Learning with Sample Complexity Analysis
by Yudan Wang, Shaofeng Zou, Yue Wang
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Distributionally Robust Reinforcement Learning (DR-RL) algorithm leverages the Multi-level Monte Carlo (MLMC) technique to optimize the worst-case performance within a predefined uncertainty set. This model-free approach integrates a threshold mechanism, ensuring finite sample requirements for implementation and improving upon previous algorithms. The paper develops methods for uncertainty sets defined by total variation, Chi-square divergence, and KL divergence, providing finite sample analyses under all three cases. The proposed algorithm represents the first model-free DR-RL approach featuring finite sample complexity for total variation and Chi-square divergence uncertainty sets, while offering an improved sample complexity and broader applicability compared to existing algorithms. The complexities of the method establish the tightest results for all three uncertainty models in model-free DR-RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to teach machines to make decisions when things don’t go as planned. Instead of relying on complex models, it uses a different approach that can work with incomplete or uncertain information. This “distributionally robust” reinforcement learning algorithm is designed to perform well even in situations where the outcome is uncertain. The researchers developed a model-free algorithm that can be used in various scenarios and provided proof that it works efficiently and effectively. |
Keywords
* Artificial intelligence * Reinforcement learning
