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..
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
Authors: Matteo Turchetta, Felix Berkenkamp, Andreas Krause
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our method on digital terrain models for the task of exploring an unknown map with a rover. In this section, we demonstrate Algorithm 1 on an exploration task. We consider the setting in [14], the exploration of the surface of Mars with a rover. |
| Researcher Affiliation | Academia | Matteo Turchetta ETH Zurich EMAIL Felix Berkenkamp ETH Zurich EMAIL Andreas Krause ETH Zurich EMAIL |
| Pseudocode | Yes | Algorithm 1 Safe exploration in MDPs (Safe MDP) |
| Open Source Code | Yes | The code for the experiments is available at http://github.com/befelix/Safe MDP. |
| Open Datasets | Yes | In our experiments we use digital terrain models of the surface of Mars from the High Resolution Imaging Science Experiment (Hi RISE), which have a resolution of one meter [12]. |
| Dataset Splits | No | The paper describes an exploration algorithm and its evaluation, but does not provide specific training/validation/test dataset splits for reproducibility in the traditional sense of supervised learning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'GP framework' and 'Matérn kernel' but does not specify particular software libraries or their version numbers (e.g., Python, PyTorch, scikit-learn, etc.) that would be needed for replication. |
| Experiment Setup | Yes | The lengthscales are set to 14.5 m and the prior standard deviation of heights is 10 m. We assume a noise standard deviation of 0.075 m. we fix βt to a constant value, βt = 2, 8t 0. |