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..
Information-Theoretic Safe Exploration with Gaussian Processes
Authors: Alessandro Bottero, Carlos Luis, Julia Vinogradska, Felix Berkenkamp, Jan R. Peters
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations demonstrate improved data-efficiency and scalability. ... In this section we empirically evaluate ISE. |
| Researcher Affiliation | Collaboration | 1Bosch Center for Artificial Intelligence, Germany 2Technische Universität Darmstadt, Germany |
| Pseudocode | Yes | Algorithm 1 Information-Theoretic Safe Exploration |
| Open Source Code | Yes | The code is available at https://github.com/boschresearch/ information-theoretic-safe-exploration. |
| Open Datasets | No | The paper uses either generated GP samples or interaction data from the Open AI Gym framework. It cites the Open AI Gym framework but does not provide a direct link or citation to a specific publicly available dataset used for training/evaluation. |
| Dataset Splits | No | The paper does not specify traditional train/validation/test dataset splits (e.g., percentages or counts). Experiments involve iterative data collection and evaluation within an exploration process rather than predefined splits. |
| Hardware Specification | Yes | All experiments were performed on either a desktop PC with an Intel i7-2600 CPU and a single NVIDIA RTX 2080 Ti GPU, or on an internal cluster running on machines with Intel Xeon E5-2630 CPUs. |
| Software Dependencies | No | The paper mentions using PyTorch and GPyTorch for implementation but does not specify their version numbers. |
| Experiment Setup | Yes | As commonly done in the literature (see Section 5), we set βn = 2 for all experiments. ... We select 100 samples from a two-dimensional GP with RBF kernel, defined in [ 2.5, 2.5] [ 2.5, 2.5] and run ISE and STAGEOPT for 100 iterations for each sample. ... For the inverted pendulum task, we used an episode length of 200 time steps and a threshold θM = 1.5 rad/s. For the cart pole task, we used an episode length of 200 time steps and a threshold θM = 0.2 rad. |