Summary of Near-optimal Dynamic Regret For Adversarial Linear Mixture Mdps, by Long-fei Li et al.
Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
by Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposes an algorithm to address episodic linear mixture Markov Decision Processes (MDPs) with unknown transitions and adversarial rewards. The authors analyze two existing methods, occupancy-measure-based and policy-based, highlighting their strengths and limitations. They then develop a novel method that combines the benefits of both approaches, employing a two-layer structure for global optimization and variance-aware value-targeted regression to tackle the unknown transition. This approach achieves an (d + ) dynamic regret, which is minimax optimal up to logarithmic factors. The algorithm is shown to be effective in handling non-stationary environments and unknown transitions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to deal with complex decision-making problems where the environment changes over time and the rewards are not always helpful. Researchers have developed two main approaches to tackle this challenge, but they each have their limitations. The authors propose a new way to combine these methods, which can handle changing environments and unknown transitions better than the previous methods. This new approach is able to achieve near-optimal performance in certain situations. |
Keywords
» Artificial intelligence » Optimization » Regression
