Summary of Prior-dependent Analysis Of Posterior Sampling Reinforcement Learning with Function Approximation, by Yingru Li and Zhi-quan Luo
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation
by Yingru Li, Zhi-Quan Luo
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 advances randomized exploration in reinforcement learning (RL) with function approximation modeled by linear mixture MDPs. It establishes the first prior-dependent Bayesian regret bound for RL with function approximation, presenting an upper bound of O(d√H^3T log T), where d represents the dimensionality of the transition kernel, H is the planning horizon, and T is the total number of interactions. This signifies a methodological enhancement by optimizing the O(√log T) factor over the previous benchmark specified to linear mixture MDPs. The approach leverages a value-targeted model learning perspective, introducing a decoupling argument and a variance reduction technique to formalize Bayesian regret bounds more effectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes big progress in how computers learn from experiences. It helps us understand how to make better choices when we don’t know the rules of the game beforehand. The authors use math to show that their new way of doing things is better than what other people have done before. They also explain why this is important and how it can help us make robots or computers that are really good at learning from experiences. |
Keywords
» Artificial intelligence » Reinforcement learning
