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].
POMDPs in Continuous Time and Discrete Spaces
Authors: Bastian Alt, Matthias Schultheis, Heinz Koeppl
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the applicability on a set of toy examples which pave the way for future methods providing solutions for high dimensional problems. |
| Researcher Affiliation | Academia | Technische Universität Darmstadt EMAIL |
| Pseudocode | No | The paper refers to algorithms but does not contain a clearly labeled pseudocode or algorithm block within the provided text. |
| Open Source Code | Yes | An implementation of our proposed method is publicly available1. 1https://git.rwth-aachen.de/bcs/pomdps_continuous_time_discrete_spaces |
| Open Datasets | No | The paper uses adapted 'toy examples' and describes problem setups rather than relying on and providing access information for a public, pre-existing dataset. |
| Dataset Splits | No | The paper describes learning on simulated trajectories and does not mention specific train/validation/test dataset splits with percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using deep learning techniques but does not provide specific software dependencies or their version numbers required to replicate the experiment. |
| Experiment Setup | No | The paper describes the experimental tasks and learning algorithms but does not provide specific details such as hyperparameter values, batch sizes, learning rates, or optimizer settings for the neural networks used in the experiments. |