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 [1].
Path Planning Problems with Side Observations—When Colonels Play Hide-and-Seek
Authors: Dong Quan Vu, Patrick Loiseau, Alonso Silva, Long Tran-Thanh2252-2259
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conducted several numerical experiments that compare the running time and the actual expected regret of EXP3-OE and FPL-IX in CB and HS games. The numerical results are consistent with theoretical results in this work. Our code for these experiments can be found at https://github.com/dongquan11/CB-HS.SOPPP. |
| Researcher Affiliation | Collaboration | 1Nokia Bell Labs France, AAAID Department, 2Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG & MPI-SWS, 3Safran Tech, Signal and Information Technologies, 4Univ. of Southampton, School of Electronics and Computer Science |
| Pseudocode | Yes | Algorithm 1 EXP3-OE Algorithm for SOPPP. |
| Open Source Code | Yes | Our code for these experiments can be found at https://github.com/dongquan11/CB-HS.SOPPP. |
| Open Datasets | No | The paper describes online learning in adversarial games where losses are chosen at each stage, rather than using a static, publicly available dataset with a training split. |
| Dataset Splits | No | The paper focuses on online learning in an adversarial setting and does not mention a traditional validation split for dataset-based experiments. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., "Python 3.8", "PyTorch 1.9"). |
| Experiment Setup | No | The paper defines parameters like T, η, and β for the EXP3-OE algorithm, but it does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, epochs), optimizers, or other system-level training settings common in empirical machine learning papers. |