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 [1].
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. |