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..
Alternating Optimisation and Quadrature for Robust Control
Authors: Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael Osborne, Shimon Whiteson
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results across different domains show that ALOQ can learn more efficiently and robustly than existing methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science, 3Department of Engineering Science, University of Oxford 2Inria, Villers-l es-Nancy, France; CNRS/Universit e de Lorraine, Loria, UMR 7503, Vandœuvre-l es-Nancy, France |
| Pseudocode | Yes | Algorithm 1 ALOQ |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The experiments are conducted on a 'simulated robot arm control task' and a 'hexapod locomotion task', which are described as simulators. No publicly available datasets are mentioned for these simulations. |
| Dataset Splits | No | The paper describes experiments in simulators and does not explicitly provide training/test/validation dataset splits. It mentions '20 independent runs' and 'batch size of 5 trajectories' for RL settings, but not fixed dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, specific libraries and their versions). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, optimizer settings, or detailed training configurations. |