Generalized Nested Rollout Policy Adaptation with Limited Repetitions

Tristan Cazenave
1/18/2024
cs.AI

No confident code implementation found for this paper.

Abstract

Generalized Nested Rollout Policy Adaptation (GNRPA) is a Monte Carlo search algorithm for optimizing a sequence of choices. We propose to improve on GNRPA by avoiding too deterministic policies that find again and again the same sequence of choices. We do so by limiting the number of repetitions of the best sequence found at a given level. Experiments show that it improves the algorithm for three different combinatorial problems: Inverse RNA Folding, the Traveling Salesman Problem with Time Windows and the Weak Schur problem.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Cite this paper

@article{cazenave2024generalized,
  title  = {Generalized Nested Rollout Policy Adaptation with Limited Repetitions},
  author = {Tristan Cazenave},
  year   = {2024},
  eprint = {2401.10420},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.10420v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.10420.svg)](https://codeofpaper.com/paper/2401.10420)

Discussion