Summary of Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes, by Asaf Cassel and Aviv Rosenberg
Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes
by Asaf Cassel, Aviv Rosenberg
First submitted to arxiv on: 3 Jul 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 This paper proposes an improvement to Policy Optimization (PO) methods, a popular type of Reinforcement Learning algorithm. The authors, building on previous work by Sherman et al. [2023a], aim to eliminate the costly warm-up phase required in existing PO algorithms. Instead, they introduce a simple and efficient contraction mechanism that achieves rate-optimal regret with improved dependence on key problem parameters. This is achieved through two fundamental settings: adversarial losses with full-information feedback and stochastic losses with bandit feedback. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes Reinforcement Learning more practical by simplifying a popular algorithm called Policy Optimization (PO). PO helps make decisions when there are many options and uncertain outcomes. The previous way of using PO was slow because it needed a long warm-up phase. This new approach skips that step and is faster and more efficient. It works in two different situations: when the outcome is certain, and when it’s random. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
