Generalized Nested Rollout Policy Adaptation with Limited Repetitions
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 39 references that are in the map. First posted on arXiv 18 Jan 2024.
Builds on
8 of 39 references.
References · 8
- 2017Attention Is All You Needcited by 59,932
- 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
- 2017Designing RNA Secondary Structures is Hardcited by 15
- 2020Generalized Nested Rollout Policy Adaptationcited by 9
- 2020Monte Carlo Inverse Foldingcited by 5
- 2021Stabilized Nested Rollout Policy Adaptationcited by 3
- 2022Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embeddingcited by 3
- 2021Generalized Nested Rollout Policy Adaptation with Dynamic Bias for Vehicle Routingcited by 2
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.