Summary of Generalized Nested Rollout Policy Adaptation with Limited Repetitions, by Tristan Cazenave
Generalized Nested Rollout Policy Adaptation with Limited Repetitions
by Tristan Cazenave
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed Generalized Nested Rollout Policy Adaptation (GNRPA) algorithm optimizes a sequence of choices using Monte Carlo search. To improve upon GNRPA, the authors introduce a limitation on the number of repetitions of the best sequence found at each level, reducing the determinism of policies. Experimental results demonstrate the effectiveness of this approach for three combinatorial problems: Inverse RNA Folding, Traveling Salesman Problem with Time Windows, and Weak Schur problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves an algorithm called Generalized Nested Rollout Policy Adaptation (GNRPA) that helps make good choices by trying different options. The goal is to find a better way to choose between options without getting stuck in the same routine. To do this, the authors set a limit on how many times they repeat the best choice at each level. This makes it easier to try new things and explore more possibilities. The results show that this approach works well for solving three complex problems. |
