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..
Local policy search with Bayesian optimization
Authors: Sarah Müller, Alexander von Rohr, Sebastian Trimpe
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The comparison reveals improved sample complexity and reduced variance in extensive empirical evaluations on synthetic objectives. Further, we highlight the benefits of active sampling on popular RL benchmarks. |
| Researcher Affiliation | Collaboration | 1Max Planck Institute for Intelligent Systems, Stuttgart, Germany 2Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Germany 3IAV Gmb H, Gifhorn, Germany 4 Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany |
| Pseudocode | Yes | Algorithm 1 GIBO |
| Open Source Code | Yes | All data and source code necessary to reproduce the results are published at https://github.com/sarmueller/gibo. |
| Open Datasets | Yes | Lastly, we evaluate the performance of GIBO on classic control and Mu Jo Co tasks included in the Open AI Gym [35, 36]. |
| Dataset Splits | No | The paper describes using synthetic functions for 'within-model comparison' and RL environments (Gym and MuJoCo) for evaluation, showing 'training curves'. However, it does not provide explicit numerical train/validation/test splits (e.g., percentages or sample counts) or specify how data was partitioned for these purposes beyond general evaluation settings. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'Bo Torch' and 'Gpytorch' but does not specify their version numbers or any other software dependencies with version details. |
| Experiment Setup | Yes | All algorithms were started in the middle of the domain x0 = [0.5]d and had a limited budget of 300 noised function evaluations. The noise was Gaussian distributed with standard deviation σ = 0.1. |