# Generalized Nested Rollout Policy Adaptation with Limited Repetitions

arXiv: https://arxiv.org/abs/2401.10420 · HTML: https://mlsanity.com/paper/2401.10420/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Designing RNA Secondary Structures is Hard](https://mlsanity.com/paper/1710.11513/) (2017)
- [Generalized Nested Rollout Policy Adaptation](https://mlsanity.com/paper/2003.10024/) (2020)
- [Monte Carlo Inverse Folding](https://mlsanity.com/paper/2005.09961/) (2020)
- [Stabilized Nested Rollout Policy Adaptation](https://mlsanity.com/paper/2101.03563/) (2021)
- [Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding](https://mlsanity.com/paper/2202.13706/) (2022)
- [Generalized Nested Rollout Policy Adaptation with Dynamic Bias for Vehicle Routing](https://mlsanity.com/paper/2111.06928/) (2021)

## Built on by (most cited first, 3 of 3)

- [The Montparnasse Algorithm for RNA Design](https://mlsanity.com/paper/2606.07562/) (2026)
- [Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem](https://mlsanity.com/paper/2505.08451/) (2025)
- [Pareto-NRPA: A Novel Monte-Carlo Search Algorithm for Multi-Objective Optimization](https://mlsanity.com/paper/2507.19109/) (2025)

## Abstract (arXiv)

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.