Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Warm-Starting Nested Rollout Policy Adaptation with Optimal Stopping
Authors: Chen Dang, Cristina Bazgan, Tristan Cazenave, Morgan Chopin, Pierre-Henri Wuillemin
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The fourth section gives experimental results for the Minimum Congestion Shortest Path Routing problem, the Traveling Salesman Problem with Time Windows and the Snake-in-the-Box problem. |
| Researcher Affiliation | Collaboration | 1 Orange Labs, Chˆatillon, France 2 Universit e Paris-Dauphine, PSL Research University, CNRS, UMR 7243, LAMSADE, F-75016 Paris, France 3 Sorbonne Universit e, CNRS, UMR 7606, LIP6, F-75005 Paris, France |
| Pseudocode | Yes | Algorithm 1: The playout algorithm, Algorithm 2: The adapt algorithm, Algorithm 3: The NRPA algorithm, Algorithm 4: Meta-NRPA with one item, Algorithm 5: Meta-NRPA with α% items |
| Open Source Code | No | The paper does not provide any link or explicit statement about releasing the source code for the described methodology. |
| Open Datasets | Yes | We test our algorithms on rc204.1, which is the most difficult instance in the Solomon-Potwin-Bengio TSPTW benchmark. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits. The problems addressed are combinatorial optimization problems on specific instances, not dataset splits for machine learning tasks. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | Yes | We use NRPA with a level of 2 and 50 iterations. Each method executes 20 independent runs on each graph, the results are normalized according to the lower bound calculated by Fleischer s approximation scheme with ϵ = 0.1 (Fleischer 2000). Graphs having more than 400 nodes are executed for 2 hours, others for 30 minutes. ... We use NRPA of level 4 and 100 iterations. ... The learning rate of NRPA α is set to 0.01... We use NRPA with level 4, 100 iterations, Meta-NRPA with 10% items, and 5% for ϵ-greedy, 0.01 for learning rate α. |