Summary of Multiple-policy Evaluation Via Density Estimation, by Yilei Chen et al.
Multiple-policy Evaluation via Density Estimation
by Yilei Chen, Aldo Pacchiano, Ioannis Ch. Paschalidis
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces an algorithm called CAESAR to evaluate the performance of multiple policies with high accuracy. The goal is to estimate the expected total reward over a fixed horizon for each policy to within an error margin ε with probability at least 1 – δ. The authors propose a two-phase approach, first generating coarse estimates of visitation distributions and then approximating the optimal sampling distribution using importance weighting ratios. CAESAR achieves a sample complexity of O(H4/ε2) up to low-order and logarithmic terms, where H is the horizon. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed an algorithm called CAESAR to compare how well different policies work. They wanted to figure out which policy will do better in the long run with high accuracy. To do this, they came up with a two-step plan: first, get some rough estimates of what each policy does, and then use that information to make a more precise calculation. The new algorithm can be used to evaluate many policies at once. |
Keywords
» Artificial intelligence » Probability
