Alternating Optimisation and Quadrature for Robust Control
Authors: Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael Osborne, Shimon Whiteson
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |