Summary of Improved Algorithm For Adversarial Linear Mixture Mdps with Bandit Feedback and Unknown Transition, by Long-fei Li et al.
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition
by Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
First submitted to arxiv on: 7 Mar 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 A reinforcement learning algorithm is developed for linear function approximation with unknown transition and adversarial losses in the bandit feedback setting. The algorithm focuses on linear mixture MDPs, whose transition kernel is a linear mixture model. This new approach achieves an (d + ) regret with high probability, improving upon previous results in Zhao et al. (2023a). The advancements are attributed to a novel least square estimator for the transition parameter and a self-normalized concentration technique that handles non-independent noises. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new algorithm is created for learning with unknown transitions and noisy feedback. This approach helps make better decisions by combining information from different states, which improves previous methods. The result shows that this algorithm can make good choices and avoid mistakes. It’s an important step forward in understanding how to learn from experience when things are uncertain. |
Keywords
* Artificial intelligence * Mixture model * Probability * Reinforcement learning
